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Overview

Brought to you by YData

Dataset statistics

Number of variables68
Number of observations818
Missing cells26.804
Missing cells (%)48.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory434.7 KiB
Average record size in memory544.2 B

Variable types

Text11
Categorical36
Boolean3
Numeric18

Alerts

TREATMENT_DETAILS has constant value "Medical Therapy" Constant
SV_Status has constant value "SOMATIC" Constant
NCBI_Build has constant value "GRCh37" Constant
DNA_Support has constant value "True" Constant
RNA_Support has constant value "unknown" Constant
Normal_Variant_Count has constant value "0.0" Constant
cancer_type has constant value "Gastrointestinal Stromal Tumor" Constant
sample_class has constant value "Tumor" Constant
cancer_type_detailed has constant value "Gastrointestinal Stromal Tumor" Constant
oncotree_code has constant value "GIST" Constant
somatic_status has constant value "Matched" Constant
AGENT is highly overall correlated with EVENT_TYPE and 5 other fieldsHigh correlation
Breakpoint_Type is highly overall correlated with EVENT_TYPE and 9 other fieldsHigh correlation
Class is highly overall correlated with Connection_Type and 9 other fieldsHigh correlation
Connection_Type is highly overall correlated with Class and 9 other fieldsHigh correlation
EVENT_TYPE is highly overall correlated with AGENT and 22 other fieldsHigh correlation
NOTE is highly overall correlated with AGENT and 17 other fieldsHigh correlation
Normal_Read_Count is highly overall correlated with EVENT_TYPE and 12 other fieldsHigh correlation
START_DATE is highly overall correlated with Breakpoint_Type and 11 other fieldsHigh correlation
STOP_DATE is highly overall correlated with Breakpoint_Type and 11 other fieldsHigh correlation
SUBTYPE is highly overall correlated with AGENT and 5 other fieldsHigh correlation
SV_Length is highly overall correlated with AGENT and 13 other fieldsHigh correlation
Site1_Chromosome is highly overall correlated with EVENT_TYPE and 14 other fieldsHigh correlation
Site1_Position is highly overall correlated with EVENT_TYPE and 8 other fieldsHigh correlation
Site2_Chromosome is highly overall correlated with EVENT_TYPE and 13 other fieldsHigh correlation
Site2_Position is highly overall correlated with EVENT_TYPE and 10 other fieldsHigh correlation
TREATMENT_BEST_RESPONSE is highly overall correlated with EVENT_TYPE and 4 other fieldsHigh correlation
Tumor_Paired_End_Read_Count is highly overall correlated with EVENT_TYPE and 6 other fieldsHigh correlation
Tumor_Read_Count is highly overall correlated with EVENT_TYPE and 9 other fieldsHigh correlation
Tumor_Split_Read_Count is highly overall correlated with Breakpoint_Type and 8 other fieldsHigh correlation
Tumor_Variant_Count is highly overall correlated with EVENT_TYPE and 5 other fieldsHigh correlation
age_at_diagnosis is highly overall correlated with Breakpoint_Type and 4 other fieldsHigh correlation
first_treatment_miotic_rate_50hpf is highly overall correlated with EVENT_TYPE and 1 other fieldsHigh correlation
first_treatment_tumor_size_cm is highly overall correlated with Class and 1 other fieldsHigh correlation
gene_panel is highly overall correlated with Class and 4 other fieldsHigh correlation
institute is highly overall correlated with AGENT and 7 other fieldsHigh correlation
metastatic_site is highly overall correlated with Normal_Read_Count and 7 other fieldsHigh correlation
msi_score is highly overall correlated with Connection_Type and 1 other fieldsHigh correlation
msi_type is highly overall correlated with Site2_Chromosome and 2 other fieldsHigh correlation
os_adjuvanttherapy is highly overall correlated with Site1_Chromosome and 2 other fieldsHigh correlation
os_status is highly overall correlated with Site2_Position and 1 other fieldsHigh correlation
ped_ind is highly overall correlated with AGENT and 21 other fieldsHigh correlation
pre_therapy_group is highly overall correlated with Breakpoint_Type and 10 other fieldsHigh correlation
race is highly overall correlated with Site1_Chromosome and 1 other fieldsHigh correlation
religion is highly overall correlated with Normal_Read_Count and 2 other fieldsHigh correlation
rfs_months is highly overall correlated with EVENT_TYPEHigh correlation
rfs_status is highly overall correlated with TREATMENT_BEST_RESPONSE and 4 other fieldsHigh correlation
risk_group is highly overall correlated with Breakpoint_Type and 17 other fieldsHigh correlation
sample_coverage is highly overall correlated with Site1_ChromosomeHigh correlation
sample_type is highly overall correlated with Normal_Read_Count and 5 other fieldsHigh correlation
stage_at_diagnosis is highly overall correlated with Tumor_Paired_End_Read_Count and 2 other fieldsHigh correlation
tmb_nonsynonymous is highly overall correlated with Breakpoint_TypeHigh correlation
tumor_purity is highly overall correlated with Tumor_Split_Read_CountHigh correlation
institute is highly imbalanced (90.6%) Imbalance
ped_ind is highly imbalanced (92.6%) Imbalance
EVENT_TYPE is highly imbalanced (97.1%) Imbalance
NOTE is highly imbalanced (64.6%) Imbalance
Breakpoint_Type is highly imbalanced (54.6%) Imbalance
metastatic_site is highly imbalanced (54.7%) Imbalance
msi_type is highly imbalanced (61.3%) Imbalance
race is highly imbalanced (53.7%) Imbalance
ethnicity is highly imbalanced (80.3%) Imbalance
institute has 167 (20.4%) missing values Missing
religion has 148 (18.1%) missing values Missing
ped_ind has 148 (18.1%) missing values Missing
rfs_status has 196 (24.0%) missing values Missing
rfs_months has 196 (24.0%) missing values Missing
age_at_diagnosis has 196 (24.0%) missing values Missing
stage_at_diagnosis has 196 (24.0%) missing values Missing
first_treatment_tumor_size_cm has 196 (24.0%) missing values Missing
first_treatment_miotic_rate_50hpf has 229 (28.0%) missing values Missing
pre_therapy_group has 637 (77.9%) missing values Missing
os_adjuvanttherapy has 196 (24.0%) missing values Missing
risk_group has 576 (70.4%) missing values Missing
START_DATE has 473 (57.8%) missing values Missing
STOP_DATE has 474 (57.9%) missing values Missing
EVENT_TYPE has 473 (57.8%) missing values Missing
SUBTYPE has 474 (57.9%) missing values Missing
AGENT has 478 (58.4%) missing values Missing
TREATMENT_BEST_RESPONSE has 524 (64.1%) missing values Missing
NOTE has 718 (87.8%) missing values Missing
TREATMENT_DETAILS has 474 (57.9%) missing values Missing
SV_Status has 775 (94.7%) missing values Missing
Site1_Hugo_Symbol has 775 (94.7%) missing values Missing
Site2_Hugo_Symbol has 775 (94.7%) missing values Missing
Site1_Chromosome has 776 (94.9%) missing values Missing
Site2_Chromosome has 776 (94.9%) missing values Missing
Site1_Position has 776 (94.9%) missing values Missing
Site2_Position has 776 (94.9%) missing values Missing
Site1_Description has 776 (94.9%) missing values Missing
Site2_Description has 776 (94.9%) missing values Missing
NCBI_Build has 775 (94.7%) missing values Missing
Class has 776 (94.9%) missing values Missing
Tumor_Split_Read_Count has 792 (96.8%) missing values Missing
Tumor_Paired_End_Read_Count has 792 (96.8%) missing values Missing
Event_Info has 775 (94.7%) missing values Missing
Breakpoint_Type has 776 (94.9%) missing values Missing
Connection_Type has 776 (94.9%) missing values Missing
Annotation has 775 (94.7%) missing values Missing
DNA_Support has 791 (96.7%) missing values Missing
RNA_Support has 791 (96.7%) missing values Missing
SV_Length has 776 (94.9%) missing values Missing
Normal_Read_Count has 776 (94.9%) missing values Missing
Tumor_Read_Count has 776 (94.9%) missing values Missing
Normal_Variant_Count has 776 (94.9%) missing values Missing
Tumor_Variant_Count has 776 (94.9%) missing values Missing
Comments has 775 (94.7%) missing values Missing
tumor_purity has 26 (3.2%) missing values Missing
msi_score has 9 (1.1%) missing values Missing
msi_type has 9 (1.1%) missing values Missing
age_at_seq_reported_years has 10 (1.2%) missing values Missing
genes_mutados has 26 (3.2%) missing values Missing
race has 10 (1.2%) missing values Missing
ethnicity has 20 (2.4%) missing values Missing
os_status has 10 (1.2%) missing values Missing
os_months has 55 (6.7%) missing values Missing
rfs_months has 175 (21.4%) zeros Zeros
first_treatment_miotic_rate_50hpf has 45 (5.5%) zeros Zeros
SV_Length has 15 (1.8%) zeros Zeros
Normal_Read_Count has 15 (1.8%) zeros Zeros
Tumor_Read_Count has 15 (1.8%) zeros Zeros
msi_score has 121 (14.8%) zeros Zeros
tmb_nonsynonymous has 26 (3.2%) zeros Zeros

Reproduction

Analysis started2025-07-28 14:38:09.744899
Analysis finished2025-07-28 14:38:32.813900
Duration23.07 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct597
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:32.908505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters13.906
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique526 ?
Unique (%)64.3%

Sample

1st rowP-0000134-T01-IM3
2nd rowP-0000134-T01-IM3
3rd rowP-0000134-T01-IM3
4th rowP-0000134-T01-IM3
5th rowP-0000134-T02-IM3
ValueCountFrequency (%)
p-0004937-t03-im6 12
 
1.5%
p-0001315-t02-im5 11
 
1.3%
p-0001315-t01-im3 11
 
1.3%
p-0002276-t01-im3 8
 
1.0%
p-0007513-t03-im5 8
 
1.0%
p-0002477-t01-im3 7
 
0.9%
p-0012178-t01-im5 7
 
0.9%
p-0004937-t01-im5 6
 
0.7%
p-0000501-t02-im3 6
 
0.7%
p-0005066-t01-im5 6
 
0.7%
Other values (587) 736
90.0%
2025-07-28T11:38:33.066969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3043
21.9%
- 2454
17.6%
1 1163
 
8.4%
I 818
 
5.9%
M 818
 
5.9%
T 818
 
5.9%
P 818
 
5.9%
6 774
 
5.6%
5 648
 
4.7%
3 558
 
4.0%
Other values (5) 1994
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3043
21.9%
- 2454
17.6%
1 1163
 
8.4%
I 818
 
5.9%
M 818
 
5.9%
T 818
 
5.9%
P 818
 
5.9%
6 774
 
5.6%
5 648
 
4.7%
3 558
 
4.0%
Other values (5) 1994
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3043
21.9%
- 2454
17.6%
1 1163
 
8.4%
I 818
 
5.9%
M 818
 
5.9%
T 818
 
5.9%
P 818
 
5.9%
6 774
 
5.6%
5 648
 
4.7%
3 558
 
4.0%
Other values (5) 1994
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3043
21.9%
- 2454
17.6%
1 1163
 
8.4%
I 818
 
5.9%
M 818
 
5.9%
T 818
 
5.9%
P 818
 
5.9%
6 774
 
5.6%
5 648
 
4.7%
3 558
 
4.0%
Other values (5) 1994
14.3%
Distinct532
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:33.210923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters7.362
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique425 ?
Unique (%)52.0%

Sample

1st rowP-0000134
2nd rowP-0000134
3rd rowP-0000134
4th rowP-0000134
5th rowP-0000134
ValueCountFrequency (%)
p-0001315 22
 
2.7%
p-0004937 18
 
2.2%
p-0007268 10
 
1.2%
p-0001157 8
 
1.0%
p-0000134 8
 
1.0%
p-0002594 8
 
1.0%
p-0006104 8
 
1.0%
p-0002276 8
 
1.0%
p-0007513 8
 
1.0%
p-0008084 8
 
1.0%
Other values (522) 712
87.0%
2025-07-28T11:38:33.406134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2225
30.2%
P 818
 
11.1%
- 818
 
11.1%
1 531
 
7.2%
4 436
 
5.9%
3 431
 
5.9%
2 408
 
5.5%
5 383
 
5.2%
6 382
 
5.2%
7 330
 
4.5%
Other values (2) 600
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2225
30.2%
P 818
 
11.1%
- 818
 
11.1%
1 531
 
7.2%
4 436
 
5.9%
3 431
 
5.9%
2 408
 
5.5%
5 383
 
5.2%
6 382
 
5.2%
7 330
 
4.5%
Other values (2) 600
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2225
30.2%
P 818
 
11.1%
- 818
 
11.1%
1 531
 
7.2%
4 436
 
5.9%
3 431
 
5.9%
2 408
 
5.5%
5 383
 
5.2%
6 382
 
5.2%
7 330
 
4.5%
Other values (2) 600
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2225
30.2%
P 818
 
11.1%
- 818
 
11.1%
1 531
 
7.2%
4 436
 
5.9%
3 431
 
5.9%
2 408
 
5.5%
5 383
 
5.2%
6 382
 
5.2%
7 330
 
4.5%
Other values (2) 600
 
8.1%

institute
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)0.9%
Missing167
Missing (%)20.4%
Memory size6.5 KiB
MSKCC
632 
Other Institute
 
11
Mount Sinai St. Lukes
 
3
Ralph Lauren Center
 
2
MOUNT SINAI LABORATORY,DEPARTMENT OF PATHOLOGY,ONE GUSTAVE L. LEVY PLACE,NEW YORK, NY 10029
 
2

Length

Max length91
Median length5
Mean length5.5729647
Min length5

Characters and Unicode

Total characters3.628
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowMSKCC
2nd rowMSKCC
3rd rowMSKCC
4th rowMSKCC
5th rowMSKCC

Common Values

ValueCountFrequency (%)
MSKCC 632
77.3%
Other Institute 11
 
1.3%
Mount Sinai St. Lukes 3
 
0.4%
Ralph Lauren Center 2
 
0.2%
MOUNT SINAI LABORATORY,DEPARTMENT OF PATHOLOGY,ONE GUSTAVE L. LEVY PLACE,NEW YORK, NY 10029 2
 
0.2%
Queens Cancer Center 1
 
0.1%
(Missing) 167
 
20.4%

Length

2025-07-28T11:38:33.463586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:33.508640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mskcc 632
90.4%
other 11
 
1.6%
institute 11
 
1.6%
mount 5
 
0.7%
sinai 5
 
0.7%
st 3
 
0.4%
lukes 3
 
0.4%
center 3
 
0.4%
ralph 2
 
0.3%
lauren 2
 
0.3%
Other values (12) 22
 
3.1%

Most occurring characters

ValueCountFrequency (%)
C 1270
35.0%
S 642
17.7%
M 639
17.6%
K 634
17.5%
t 53
 
1.5%
48
 
1.3%
e 36
 
1.0%
O 27
 
0.7%
n 24
 
0.7%
u 20
 
0.6%
Other values (34) 235
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1270
35.0%
S 642
17.7%
M 639
17.6%
K 634
17.5%
t 53
 
1.5%
48
 
1.3%
e 36
 
1.0%
O 27
 
0.7%
n 24
 
0.7%
u 20
 
0.6%
Other values (34) 235
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1270
35.0%
S 642
17.7%
M 639
17.6%
K 634
17.5%
t 53
 
1.5%
48
 
1.3%
e 36
 
1.0%
O 27
 
0.7%
n 24
 
0.7%
u 20
 
0.6%
Other values (34) 235
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1270
35.0%
S 642
17.7%
M 639
17.6%
K 634
17.5%
t 53
 
1.5%
48
 
1.3%
e 36
 
1.0%
O 27
 
0.7%
n 24
 
0.7%
u 20
 
0.6%
Other values (34) 235
 
6.5%

religion
Categorical

High correlation  Missing 

Distinct22
Distinct (%)3.3%
Missing148
Missing (%)18.1%
Memory size6.5 KiB
CATHOLIC/ROMAN
232 
NONE
159 
JEWISH
66 
CHRISTIAN
51 
UNKNOWN
35 
Other values (17)
127 

Length

Max length19
Median length18
Mean length9.2597015
Min length4

Characters and Unicode

Total characters6.204
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowJEWISH
2nd rowJEWISH
3rd rowJEWISH
4th rowJEWISH
5th rowNONE

Common Values

ValueCountFrequency (%)
CATHOLIC/ROMAN 232
28.4%
NONE 159
19.4%
JEWISH 66
 
8.1%
CHRISTIAN 51
 
6.2%
UNKNOWN 35
 
4.3%
BAPTIST 24
 
2.9%
PRESBYTERIAN 15
 
1.8%
MUSLIM 15
 
1.8%
PROTESTANT 12
 
1.5%
OTHER 8
 
1.0%
Other values (12) 53
 
6.5%
(Missing) 148
18.1%

Length

2025-07-28T11:38:33.584313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
catholic/roman 232
33.5%
none 159
23.0%
jewish 66
 
9.5%
christian 56
 
8.1%
unknown 35
 
5.1%
baptist 24
 
3.5%
presbyterian 15
 
2.2%
muslim 15
 
2.2%
orthodox 13
 
1.9%
protestant 12
 
1.7%
Other values (15) 65
 
9.4%

Most occurring characters

ValueCountFrequency (%)
N 773
12.5%
O 741
11.9%
A 608
9.8%
C 531
8.6%
I 507
8.2%
T 449
 
7.2%
H 408
 
6.6%
R 368
 
5.9%
E 326
 
5.3%
M 267
 
4.3%
Other values (15) 1226
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 773
12.5%
O 741
11.9%
A 608
9.8%
C 531
8.6%
I 507
8.2%
T 449
 
7.2%
H 408
 
6.6%
R 368
 
5.9%
E 326
 
5.3%
M 267
 
4.3%
Other values (15) 1226
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 773
12.5%
O 741
11.9%
A 608
9.8%
C 531
8.6%
I 507
8.2%
T 449
 
7.2%
H 408
 
6.6%
R 368
 
5.9%
E 326
 
5.3%
M 267
 
4.3%
Other values (15) 1226
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 773
12.5%
O 741
11.9%
A 608
9.8%
C 531
8.6%
I 507
8.2%
T 449
 
7.2%
H 408
 
6.6%
R 368
 
5.9%
E 326
 
5.3%
M 267
 
4.3%
Other values (15) 1226
19.8%

ped_ind
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing148
Missing (%)18.1%
Memory size1.7 KiB
False
664 
True
 
6
(Missing)
148 
ValueCountFrequency (%)
False 664
81.2%
True 6
 
0.7%
(Missing) 148
 
18.1%
2025-07-28T11:38:33.623866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

rfs_status
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing196
Missing (%)24.0%
Memory size6.5 KiB
1:Recurrence
380 
0:No recurrence
242 

Length

Max length15
Median length12
Mean length13.167203
Min length12

Characters and Unicode

Total characters8.190
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1:Recurrence
2nd row1:Recurrence
3rd row1:Recurrence
4th row1:Recurrence
5th row1:Recurrence

Common Values

ValueCountFrequency (%)
1:Recurrence 380
46.5%
0:No recurrence 242
29.6%
(Missing) 196
24.0%

Length

2025-07-28T11:38:33.672454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:33.712927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1:recurrence 380
44.0%
0:no 242
28.0%
recurrence 242
28.0%

Most occurring characters

ValueCountFrequency (%)
e 1866
22.8%
r 1486
18.1%
c 1244
15.2%
: 622
 
7.6%
u 622
 
7.6%
n 622
 
7.6%
R 380
 
4.6%
1 380
 
4.6%
0 242
 
3.0%
N 242
 
3.0%
Other values (2) 484
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1866
22.8%
r 1486
18.1%
c 1244
15.2%
: 622
 
7.6%
u 622
 
7.6%
n 622
 
7.6%
R 380
 
4.6%
1 380
 
4.6%
0 242
 
3.0%
N 242
 
3.0%
Other values (2) 484
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1866
22.8%
r 1486
18.1%
c 1244
15.2%
: 622
 
7.6%
u 622
 
7.6%
n 622
 
7.6%
R 380
 
4.6%
1 380
 
4.6%
0 242
 
3.0%
N 242
 
3.0%
Other values (2) 484
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1866
22.8%
r 1486
18.1%
c 1244
15.2%
: 622
 
7.6%
u 622
 
7.6%
n 622
 
7.6%
R 380
 
4.6%
1 380
 
4.6%
0 242
 
3.0%
N 242
 
3.0%
Other values (2) 484
 
5.9%

rfs_months
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct96
Distinct (%)15.4%
Missing196
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean23.467846
Minimum0
Maximum217
Zeros175
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:33.771592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q335
95-th percentile77
Maximum217
Range217
Interquartile range (IQR)35

Descriptive statistics

Standard deviation32.459931
Coefficient of variation (CV)1.3831662
Kurtosis11.119468
Mean23.467846
Median Absolute Deviation (MAD)13
Skewness2.8104129
Sum14597
Variance1053.6471
MonotonicityNot monotonic
2025-07-28T11:38:33.841864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175
21.4%
5 21
 
2.6%
13 15
 
1.8%
19 15
 
1.8%
17 15
 
1.8%
1 14
 
1.7%
12 12
 
1.5%
11 12
 
1.5%
2 12
 
1.5%
9 11
 
1.3%
Other values (86) 320
39.1%
(Missing) 196
24.0%
ValueCountFrequency (%)
0 175
21.4%
1 14
 
1.7%
2 12
 
1.5%
3 8
 
1.0%
4 7
 
0.9%
5 21
 
2.6%
6 7
 
0.9%
7 10
 
1.2%
8 10
 
1.2%
9 11
 
1.3%
ValueCountFrequency (%)
217 4
0.5%
212 1
 
0.1%
165 2
0.2%
148 1
 
0.1%
146 1
 
0.1%
144 1
 
0.1%
136 1
 
0.1%
135 1
 
0.1%
127 1
 
0.1%
125 1
 
0.1%

age_at_diagnosis
Real number (ℝ)

High correlation  Missing 

Distinct71
Distinct (%)11.4%
Missing196
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean54.347267
Minimum7
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:33.913456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile29
Q145
median55
Q366
95-th percentile77
Maximum90
Range83
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.870184
Coefficient of variation (CV)0.2736142
Kurtosis-0.26238963
Mean54.347267
Median Absolute Deviation (MAD)10
Skewness-0.32998286
Sum33804
Variance221.12237
MonotonicityNot monotonic
2025-07-28T11:38:33.985148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 23
 
2.8%
56 22
 
2.7%
54 21
 
2.6%
49 21
 
2.6%
60 19
 
2.3%
59 19
 
2.3%
55 18
 
2.2%
66 17
 
2.1%
46 17
 
2.1%
53 16
 
2.0%
Other values (61) 429
52.4%
(Missing) 196
24.0%
ValueCountFrequency (%)
7 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
17 1
 
0.1%
18 1
 
0.1%
19 5
0.6%
20 1
 
0.1%
21 1
 
0.1%
22 4
0.5%
23 1
 
0.1%
ValueCountFrequency (%)
90 1
 
0.1%
85 3
 
0.4%
84 2
 
0.2%
83 3
 
0.4%
82 1
 
0.1%
80 5
0.6%
79 4
0.5%
78 7
0.9%
77 8
1.0%
76 8
1.0%

stage_at_diagnosis
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing196
Missing (%)24.0%
Memory size6.5 KiB
Localized
388 
Metastatic
234 

Length

Max length10
Median length9
Mean length9.3762058
Min length9

Characters and Unicode

Total characters5.832
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetastatic
2nd rowMetastatic
3rd rowMetastatic
4th rowMetastatic
5th rowLocalized

Common Values

ValueCountFrequency (%)
Localized 388
47.4%
Metastatic 234
28.6%
(Missing) 196
24.0%

Length

2025-07-28T11:38:34.049841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:34.085873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
localized 388
62.4%
metastatic 234
37.6%

Most occurring characters

ValueCountFrequency (%)
a 856
14.7%
t 702
12.0%
i 622
10.7%
c 622
10.7%
e 622
10.7%
o 388
6.7%
L 388
6.7%
l 388
6.7%
z 388
6.7%
d 388
6.7%
Other values (2) 468
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 856
14.7%
t 702
12.0%
i 622
10.7%
c 622
10.7%
e 622
10.7%
o 388
6.7%
L 388
6.7%
l 388
6.7%
z 388
6.7%
d 388
6.7%
Other values (2) 468
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 856
14.7%
t 702
12.0%
i 622
10.7%
c 622
10.7%
e 622
10.7%
o 388
6.7%
L 388
6.7%
l 388
6.7%
z 388
6.7%
d 388
6.7%
Other values (2) 468
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 856
14.7%
t 702
12.0%
i 622
10.7%
c 622
10.7%
e 622
10.7%
o 388
6.7%
L 388
6.7%
l 388
6.7%
z 388
6.7%
d 388
6.7%
Other values (2) 468
8.0%

first_treatment_tumor_size_cm
Real number (ℝ)

High correlation  Missing 

Distinct145
Distinct (%)23.3%
Missing196
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean9.5782958
Minimum0.7
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:34.142423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile2.4
Q15.1
median8.5
Q313
95-th percentile20
Maximum42
Range41.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation5.8824498
Coefficient of variation (CV)0.61414368
Kurtosis2.3499443
Mean9.5782958
Median Absolute Deviation (MAD)3.55
Skewness1.1967326
Sum5957.7
Variance34.603216
MonotonicityNot monotonic
2025-07-28T11:38:34.216463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 28
 
3.4%
8 27
 
3.3%
11 26
 
3.2%
10 20
 
2.4%
12 19
 
2.3%
6.5 18
 
2.2%
7 16
 
2.0%
14 15
 
1.8%
6 12
 
1.5%
2.8 12
 
1.5%
Other values (135) 429
52.4%
(Missing) 196
24.0%
ValueCountFrequency (%)
0.7 1
 
0.1%
1 1
 
0.1%
1.2 1
 
0.1%
1.4 1
 
0.1%
1.5 2
 
0.2%
1.7 3
0.4%
1.8 3
0.4%
1.9 2
 
0.2%
2 6
0.7%
2.1 2
 
0.2%
ValueCountFrequency (%)
42 1
 
0.1%
36.5 1
 
0.1%
34 1
 
0.1%
29.5 1
 
0.1%
27.7 1
 
0.1%
26 1
 
0.1%
25 8
1.0%
24 10
1.2%
21 5
0.6%
20.4 1
 
0.1%

first_treatment_miotic_rate_50hpf
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct63
Distinct (%)10.7%
Missing229
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean17.685908
Minimum0
Maximum175
Zeros45
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:34.286659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q325
95-th percentile54.2
Maximum175
Range175
Interquartile range (IQR)23

Descriptive statistics

Standard deviation23.410813
Coefficient of variation (CV)1.3236986
Kurtosis7.9000668
Mean17.685908
Median Absolute Deviation (MAD)7
Skewness2.4208412
Sum10417
Variance548.06614
MonotonicityNot monotonic
2025-07-28T11:38:34.360448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 61
 
7.5%
1 55
 
6.7%
0 45
 
5.5%
5 38
 
4.6%
10 29
 
3.5%
4 27
 
3.3%
50 27
 
3.3%
20 24
 
2.9%
3 23
 
2.8%
7 23
 
2.8%
Other values (53) 237
29.0%
(Missing) 229
28.0%
ValueCountFrequency (%)
0 45
5.5%
1 55
6.7%
2 61
7.5%
3 23
 
2.8%
4 27
3.3%
5 38
4.6%
6 22
 
2.7%
7 23
 
2.8%
8 15
 
1.8%
9 5
 
0.6%
ValueCountFrequency (%)
175 1
 
0.1%
145 1
 
0.1%
130 1
 
0.1%
125 1
 
0.1%
112 1
 
0.1%
104 1
 
0.1%
102 1
 
0.1%
100 4
0.5%
95 1
 
0.1%
90 7
0.9%

pre_therapy_group
Categorical

High correlation  Missing 

Distinct2
Distinct (%)1.1%
Missing637
Missing (%)77.9%
Memory size6.5 KiB
Neoadjuvant
123 
Palliative
58 

Length

Max length11
Median length11
Mean length10.679558
Min length10

Characters and Unicode

Total characters1.933
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeoadjuvant
2nd rowNeoadjuvant
3rd rowNeoadjuvant
4th rowNeoadjuvant
5th rowPalliative

Common Values

ValueCountFrequency (%)
Neoadjuvant 123
 
15.0%
Palliative 58
 
7.1%
(Missing) 637
77.9%

Length

2025-07-28T11:38:34.431764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:34.474095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
neoadjuvant 123
68.0%
palliative 58
32.0%

Most occurring characters

ValueCountFrequency (%)
a 362
18.7%
e 181
9.4%
v 181
9.4%
t 181
9.4%
N 123
 
6.4%
d 123
 
6.4%
o 123
 
6.4%
u 123
 
6.4%
j 123
 
6.4%
n 123
 
6.4%
Other values (3) 290
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1933
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 362
18.7%
e 181
9.4%
v 181
9.4%
t 181
9.4%
N 123
 
6.4%
d 123
 
6.4%
o 123
 
6.4%
u 123
 
6.4%
j 123
 
6.4%
n 123
 
6.4%
Other values (3) 290
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1933
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 362
18.7%
e 181
9.4%
v 181
9.4%
t 181
9.4%
N 123
 
6.4%
d 123
 
6.4%
o 123
 
6.4%
u 123
 
6.4%
j 123
 
6.4%
n 123
 
6.4%
Other values (3) 290
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1933
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 362
18.7%
e 181
9.4%
v 181
9.4%
t 181
9.4%
N 123
 
6.4%
d 123
 
6.4%
o 123
 
6.4%
u 123
 
6.4%
j 123
 
6.4%
n 123
 
6.4%
Other values (3) 290
15.0%

os_adjuvanttherapy
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing196
Missing (%)24.0%
Memory size1.7 KiB
True
427 
False
195 
(Missing)
196 
ValueCountFrequency (%)
True 427
52.2%
False 195
23.8%
(Missing) 196
24.0%
2025-07-28T11:38:34.498409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

risk_group
Categorical

High correlation  Missing 

Distinct4
Distinct (%)1.7%
Missing576
Missing (%)70.4%
Memory size6.5 KiB
Moderate
149 
High
62 
Low
30 
Unknown
 
1

Length

Max length8
Median length8
Mean length6.3512397
Min length3

Characters and Unicode

Total characters1.537
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowModerate
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
Moderate 149
 
18.2%
High 62
 
7.6%
Low 30
 
3.7%
Unknown 1
 
0.1%
(Missing) 576
70.4%

Length

2025-07-28T11:38:34.546836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:34.589273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
moderate 149
61.6%
high 62
25.6%
low 30
 
12.4%
unknown 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 298
19.4%
o 180
11.7%
M 149
9.7%
d 149
9.7%
r 149
9.7%
a 149
9.7%
t 149
9.7%
H 62
 
4.0%
i 62
 
4.0%
g 62
 
4.0%
Other values (6) 128
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 298
19.4%
o 180
11.7%
M 149
9.7%
d 149
9.7%
r 149
9.7%
a 149
9.7%
t 149
9.7%
H 62
 
4.0%
i 62
 
4.0%
g 62
 
4.0%
Other values (6) 128
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 298
19.4%
o 180
11.7%
M 149
9.7%
d 149
9.7%
r 149
9.7%
a 149
9.7%
t 149
9.7%
H 62
 
4.0%
i 62
 
4.0%
g 62
 
4.0%
Other values (6) 128
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 298
19.4%
o 180
11.7%
M 149
9.7%
d 149
9.7%
r 149
9.7%
a 149
9.7%
t 149
9.7%
H 62
 
4.0%
i 62
 
4.0%
g 62
 
4.0%
Other values (6) 128
8.3%

START_DATE
Real number (ℝ)

High correlation  Missing 

Distinct236
Distinct (%)68.4%
Missing473
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean1770.7739
Minimum-967
Maximum40407
Zeros8
Zeros (%)1.0%
Negative29
Negative (%)3.5%
Memory size6.5 KiB
2025-07-28T11:38:34.655900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-967
5-th percentile-7.8
Q143
median944
Q32108
95-th percentile3875
Maximum40407
Range41374
Interquartile range (IQR)2065

Descriptive statistics

Standard deviation4800.8652
Coefficient of variation (CV)2.7111678
Kurtosis56.480069
Mean1770.7739
Median Absolute Deviation (MAD)922
Skewness7.3743932
Sum610917
Variance23048306
MonotonicityNot monotonic
2025-07-28T11:38:34.728312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
1.0%
19 5
 
0.6%
-11 5
 
0.6%
2160 4
 
0.5%
-1 4
 
0.5%
2615 4
 
0.5%
1976 4
 
0.5%
1999 4
 
0.5%
14 4
 
0.5%
345 4
 
0.5%
Other values (226) 299
36.6%
(Missing) 473
57.8%
ValueCountFrequency (%)
-967 1
 
0.1%
-213 1
 
0.1%
-46 1
 
0.1%
-39 3
0.4%
-29 1
 
0.1%
-18 1
 
0.1%
-16 1
 
0.1%
-12 1
 
0.1%
-11 5
0.6%
-9 2
 
0.2%
ValueCountFrequency (%)
40407 1
0.1%
40284 1
0.1%
40223 1
0.1%
39615 1
0.1%
39462 1
0.1%
5199 1
0.1%
5049 2
0.2%
5016 1
0.1%
4866 2
0.2%
4590 1
0.1%

STOP_DATE
Real number (ℝ)

High correlation  Missing 

Distinct254
Distinct (%)73.8%
Missing474
Missing (%)57.9%
Infinite0
Infinite (%)0.0%
Mean2277.657
Minimum4
Maximum40688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:35.017192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile186.5
Q1702
median1485.5
Q32560.5
95-th percentile4688.85
Maximum40688
Range40684
Interquartile range (IQR)1858.5

Descriptive statistics

Standard deviation4770.4845
Coefficient of variation (CV)2.0944701
Kurtosis55.814254
Mean2277.657
Median Absolute Deviation (MAD)883
Skewness7.3156485
Sum783514
Variance22757523
MonotonicityNot monotonic
2025-07-28T11:38:35.126215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
607 5
 
0.6%
214 5
 
0.6%
2615 4
 
0.5%
1186 4
 
0.5%
2676 4
 
0.5%
2037 4
 
0.5%
1968 4
 
0.5%
388 4
 
0.5%
1177 3
 
0.4%
2464 3
 
0.4%
Other values (244) 304
37.2%
(Missing) 474
57.9%
ValueCountFrequency (%)
4 1
0.1%
7 1
0.1%
56 1
0.1%
61 1
0.1%
80 2
0.2%
83 1
0.1%
92 2
0.2%
118 2
0.2%
136 1
0.1%
139 1
0.1%
ValueCountFrequency (%)
40688 1
0.1%
40345 1
0.1%
40284 1
0.1%
39705 1
0.1%
39615 1
0.1%
5453 2
0.2%
5200 1
0.1%
5169 1
0.1%
5021 1
0.1%
5019 2
0.2%

EVENT_TYPE
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.6%
Missing473
Missing (%)57.8%
Memory size6.5 KiB
TREATMENT
344 
SURGERY
 
1

Length

Max length9
Median length9
Mean length8.9942029
Min length7

Characters and Unicode

Total characters3.103
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowTREATMENT
2nd rowTREATMENT
3rd rowTREATMENT
4th rowTREATMENT
5th rowTREATMENT

Common Values

ValueCountFrequency (%)
TREATMENT 344
42.1%
SURGERY 1
 
0.1%
(Missing) 473
57.8%

Length

2025-07-28T11:38:35.205989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:35.251029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
treatment 344
99.7%
surgery 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
T 1032
33.3%
E 689
22.2%
R 346
 
11.2%
A 344
 
11.1%
M 344
 
11.1%
N 344
 
11.1%
S 1
 
< 0.1%
U 1
 
< 0.1%
G 1
 
< 0.1%
Y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1032
33.3%
E 689
22.2%
R 346
 
11.2%
A 344
 
11.1%
M 344
 
11.1%
N 344
 
11.1%
S 1
 
< 0.1%
U 1
 
< 0.1%
G 1
 
< 0.1%
Y 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1032
33.3%
E 689
22.2%
R 346
 
11.2%
A 344
 
11.1%
M 344
 
11.1%
N 344
 
11.1%
S 1
 
< 0.1%
U 1
 
< 0.1%
G 1
 
< 0.1%
Y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1032
33.3%
E 689
22.2%
R 346
 
11.2%
A 344
 
11.1%
M 344
 
11.1%
N 344
 
11.1%
S 1
 
< 0.1%
U 1
 
< 0.1%
G 1
 
< 0.1%
Y 1
 
< 0.1%

SUBTYPE
Categorical

High correlation  Missing 

Distinct10
Distinct (%)2.9%
Missing474
Missing (%)57.9%
Memory size6.5 KiB
LINE 1 PRE IMPACT
106 
LINE 1 POST IMPACT
83 
LINE 2 PRE IMPACT
52 
LINE 3 PRE IMPACT
32 
LINE 2 POST IMPACT
25 
Other values (5)
46 

Length

Max length18
Median length17
Mean length17.354651
Min length17

Characters and Unicode

Total characters5.970
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLINE 1 PRE IMPACT
2nd rowLINE 2 POST IMPACT
3rd rowLINE 3 POST IMPACT
4th rowLINE 1 POST IMPACT
5th rowLINE 1 PRE IMPACT

Common Values

ValueCountFrequency (%)
LINE 1 PRE IMPACT 106
 
13.0%
LINE 1 POST IMPACT 83
 
10.1%
LINE 2 PRE IMPACT 52
 
6.4%
LINE 3 PRE IMPACT 32
 
3.9%
LINE 2 POST IMPACT 25
 
3.1%
LINE 4 PRE IMPACT 20
 
2.4%
LINE 3 POST IMPACT 14
 
1.7%
LINE 5 PRE IMPACT 6
 
0.7%
LINE 6 PRE IMPACT 3
 
0.4%
LINE 7 PRE IMPACT 3
 
0.4%
(Missing) 474
57.9%

Length

2025-07-28T11:38:35.299865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:35.362311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
line 344
25.0%
impact 344
25.0%
pre 222
16.1%
1 189
13.7%
post 122
 
8.9%
2 77
 
5.6%
3 46
 
3.3%
4 20
 
1.5%
5 6
 
0.4%
6 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1032
17.3%
I 688
11.5%
P 688
11.5%
E 566
9.5%
T 466
7.8%
L 344
 
5.8%
N 344
 
5.8%
M 344
 
5.8%
A 344
 
5.8%
C 344
 
5.8%
Other values (10) 810
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1032
17.3%
I 688
11.5%
P 688
11.5%
E 566
9.5%
T 466
7.8%
L 344
 
5.8%
N 344
 
5.8%
M 344
 
5.8%
A 344
 
5.8%
C 344
 
5.8%
Other values (10) 810
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1032
17.3%
I 688
11.5%
P 688
11.5%
E 566
9.5%
T 466
7.8%
L 344
 
5.8%
N 344
 
5.8%
M 344
 
5.8%
A 344
 
5.8%
C 344
 
5.8%
Other values (10) 810
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1032
17.3%
I 688
11.5%
P 688
11.5%
E 566
9.5%
T 466
7.8%
L 344
 
5.8%
N 344
 
5.8%
M 344
 
5.8%
A 344
 
5.8%
C 344
 
5.8%
Other values (10) 810
13.6%

AGENT
Categorical

High correlation  Missing 

Distinct25
Distinct (%)7.4%
Missing478
Missing (%)58.4%
Memory size6.5 KiB
IMATINIB
132 
SUNITINIB
50 
CLINICAL TRIAL
37 
REGORAFENIB
34 
SORAFENIB
16 
Other values (20)
71 

Length

Max length43
Median length33
Mean length9.9058824
Min length2

Characters and Unicode

Total characters3.368
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)2.6%

Sample

1st rowIMATINIB
2nd rowREGORAFENIB
3rd rowCLINICAL TRIAL
4th rowSUNITINIB AND EVEROLIMUS
5th rowIMATINIB

Common Values

ValueCountFrequency (%)
IMATINIB 132
 
16.1%
SUNITINIB 50
 
6.1%
CLINICAL TRIAL 37
 
4.5%
REGORAFENIB 34
 
4.2%
SORAFENIB 16
 
2.0%
CT 14
 
1.7%
PAZOPANIB 13
 
1.6%
SUNITINIB AND EVEROLIMUS 9
 
1.1%
NILOTINIB 7
 
0.9%
braf& mekI 4
 
0.5%
Other values (15) 24
 
2.9%
(Missing) 478
58.4%

Length

2025-07-28T11:38:35.456470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
imatinib 132
31.1%
sunitinib 59
13.9%
clinical 40
 
9.4%
trial 38
 
9.0%
regorafenib 34
 
8.0%
sorafenib 16
 
3.8%
ct 14
 
3.3%
pazopanib 14
 
3.3%
everolimus 10
 
2.4%
and 9
 
2.1%
Other values (23) 58
13.7%

Most occurring characters

ValueCountFrequency (%)
I 834
24.8%
N 398
11.8%
A 311
 
9.2%
B 275
 
8.2%
T 265
 
7.9%
M 158
 
4.7%
R 150
 
4.5%
L 145
 
4.3%
E 125
 
3.7%
S 103
 
3.1%
Other values (34) 604
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 834
24.8%
N 398
11.8%
A 311
 
9.2%
B 275
 
8.2%
T 265
 
7.9%
M 158
 
4.7%
R 150
 
4.5%
L 145
 
4.3%
E 125
 
3.7%
S 103
 
3.1%
Other values (34) 604
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 834
24.8%
N 398
11.8%
A 311
 
9.2%
B 275
 
8.2%
T 265
 
7.9%
M 158
 
4.7%
R 150
 
4.5%
L 145
 
4.3%
E 125
 
3.7%
S 103
 
3.1%
Other values (34) 604
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 834
24.8%
N 398
11.8%
A 311
 
9.2%
B 275
 
8.2%
T 265
 
7.9%
M 158
 
4.7%
R 150
 
4.5%
L 145
 
4.3%
E 125
 
3.7%
S 103
 
3.1%
Other values (34) 604
17.9%

TREATMENT_BEST_RESPONSE
Categorical

High correlation  Missing 

Distinct7
Distinct (%)2.4%
Missing524
Missing (%)64.1%
Memory size6.5 KiB
NO
112 
YES
75 
YES(pr/sd X >/=6 MONTHS)
64 
UNKNOWN
22 
ADJ
13 
Other values (2)
 
8

Length

Max length24
Median length7
Mean length7.5782313
Min length1

Characters and Unicode

Total characters2.228
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowYES(pr/sd X >/=6 MONTHS)
2nd rowNO
3rd rowNO
4th rowNO
5th rowYES(pr/sd X >/=6 MONTHS)

Common Values

ValueCountFrequency (%)
NO 112
 
13.7%
YES 75
 
9.2%
YES(pr/sd X >/=6 MONTHS) 64
 
7.8%
UNKNOWN 22
 
2.7%
ADJ 13
 
1.6%
PENDING 7
 
0.9%
? 1
 
0.1%
(Missing) 524
64.1%

Length

2025-07-28T11:38:35.515277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:35.566013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 112
23.0%
yes 75
15.4%
yes(pr/sd 64
13.2%
x 64
13.2%
6 64
13.2%
months 64
13.2%
unknown 22
 
4.5%
adj 13
 
2.7%
pending 7
 
1.4%
1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 256
 
11.5%
S 203
 
9.1%
O 198
 
8.9%
192
 
8.6%
E 146
 
6.6%
Y 139
 
6.2%
/ 128
 
5.7%
( 64
 
2.9%
r 64
 
2.9%
p 64
 
2.9%
Other values (20) 774
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 256
 
11.5%
S 203
 
9.1%
O 198
 
8.9%
192
 
8.6%
E 146
 
6.6%
Y 139
 
6.2%
/ 128
 
5.7%
( 64
 
2.9%
r 64
 
2.9%
p 64
 
2.9%
Other values (20) 774
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 256
 
11.5%
S 203
 
9.1%
O 198
 
8.9%
192
 
8.6%
E 146
 
6.6%
Y 139
 
6.2%
/ 128
 
5.7%
( 64
 
2.9%
r 64
 
2.9%
p 64
 
2.9%
Other values (20) 774
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 256
 
11.5%
S 203
 
9.1%
O 198
 
8.9%
192
 
8.6%
E 146
 
6.6%
Y 139
 
6.2%
/ 128
 
5.7%
( 64
 
2.9%
r 64
 
2.9%
p 64
 
2.9%
Other values (20) 774
34.7%

NOTE
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)4.0%
Missing718
Missing (%)87.8%
Memory size6.5 KiB
ONGOING
88 
0NGOING
 
5
NO END DATE
 
5
UNKNOWN
 
2

Length

Max length11
Median length7
Mean length7.2
Min length7

Characters and Unicode

Total characters720
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowONGOING
2nd rowONGOING
3rd rowONGOING
4th row0NGOING
5th row0NGOING

Common Values

ValueCountFrequency (%)
ONGOING 88
 
10.8%
0NGOING 5
 
0.6%
NO END DATE 5
 
0.6%
UNKNOWN 2
 
0.2%
(Missing) 718
87.8%

Length

2025-07-28T11:38:35.635171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:35.683244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ongoing 88
80.0%
0ngoing 5
 
4.5%
no 5
 
4.5%
end 5
 
4.5%
date 5
 
4.5%
unknown 2
 
1.8%

Most occurring characters

ValueCountFrequency (%)
N 202
28.1%
O 188
26.1%
G 186
25.8%
I 93
12.9%
10
 
1.4%
D 10
 
1.4%
E 10
 
1.4%
0 5
 
0.7%
A 5
 
0.7%
T 5
 
0.7%
Other values (3) 6
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 202
28.1%
O 188
26.1%
G 186
25.8%
I 93
12.9%
10
 
1.4%
D 10
 
1.4%
E 10
 
1.4%
0 5
 
0.7%
A 5
 
0.7%
T 5
 
0.7%
Other values (3) 6
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 202
28.1%
O 188
26.1%
G 186
25.8%
I 93
12.9%
10
 
1.4%
D 10
 
1.4%
E 10
 
1.4%
0 5
 
0.7%
A 5
 
0.7%
T 5
 
0.7%
Other values (3) 6
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 202
28.1%
O 188
26.1%
G 186
25.8%
I 93
12.9%
10
 
1.4%
D 10
 
1.4%
E 10
 
1.4%
0 5
 
0.7%
A 5
 
0.7%
T 5
 
0.7%
Other values (3) 6
 
0.8%

TREATMENT_DETAILS
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.3%
Missing474
Missing (%)57.9%
Memory size6.5 KiB
Medical Therapy
344 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters5.160
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedical Therapy
2nd rowMedical Therapy
3rd rowMedical Therapy
4th rowMedical Therapy
5th rowMedical Therapy

Common Values

ValueCountFrequency (%)
Medical Therapy 344
42.1%
(Missing) 474
57.9%

Length

2025-07-28T11:38:35.751130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:35.788783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medical 344
50.0%
therapy 344
50.0%

Most occurring characters

ValueCountFrequency (%)
e 688
13.3%
a 688
13.3%
M 344
 
6.7%
i 344
 
6.7%
d 344
 
6.7%
c 344
 
6.7%
l 344
 
6.7%
344
 
6.7%
T 344
 
6.7%
h 344
 
6.7%
Other values (3) 1032
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 688
13.3%
a 688
13.3%
M 344
 
6.7%
i 344
 
6.7%
d 344
 
6.7%
c 344
 
6.7%
l 344
 
6.7%
344
 
6.7%
T 344
 
6.7%
h 344
 
6.7%
Other values (3) 1032
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 688
13.3%
a 688
13.3%
M 344
 
6.7%
i 344
 
6.7%
d 344
 
6.7%
c 344
 
6.7%
l 344
 
6.7%
344
 
6.7%
T 344
 
6.7%
h 344
 
6.7%
Other values (3) 1032
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 688
13.3%
a 688
13.3%
M 344
 
6.7%
i 344
 
6.7%
d 344
 
6.7%
c 344
 
6.7%
l 344
 
6.7%
344
 
6.7%
T 344
 
6.7%
h 344
 
6.7%
Other values (3) 1032
20.0%

SV_Status
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.3%
Missing775
Missing (%)94.7%
Memory size6.5 KiB
SOMATIC
43 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters301
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSOMATIC
2nd rowSOMATIC
3rd rowSOMATIC
4th rowSOMATIC
5th rowSOMATIC

Common Values

ValueCountFrequency (%)
SOMATIC 43
 
5.3%
(Missing) 775
94.7%

Length

2025-07-28T11:38:35.828320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:35.859333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
somatic 43
100.0%

Most occurring characters

ValueCountFrequency (%)
S 43
14.3%
O 43
14.3%
M 43
14.3%
A 43
14.3%
T 43
14.3%
I 43
14.3%
C 43
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 43
14.3%
O 43
14.3%
M 43
14.3%
A 43
14.3%
T 43
14.3%
I 43
14.3%
C 43
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 43
14.3%
O 43
14.3%
M 43
14.3%
A 43
14.3%
T 43
14.3%
I 43
14.3%
C 43
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 43
14.3%
O 43
14.3%
M 43
14.3%
A 43
14.3%
T 43
14.3%
I 43
14.3%
C 43
14.3%

Site1_Hugo_Symbol
Text

Missing 

Distinct27
Distinct (%)62.8%
Missing775
Missing (%)94.7%
Memory size6.5 KiB
2025-07-28T11:38:35.925264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.6511628
Min length3

Characters and Unicode

Total characters200
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)48.8%

Sample

1st rowKMT2D
2nd rowERBB3
3rd rowRTEL1
4th rowSRC
5th rowATM
ValueCountFrequency (%)
rtel1 7
16.3%
atm 6
 
14.0%
cdkn2a 3
 
7.0%
vtcn1 2
 
4.7%
kmt2d 2
 
4.7%
agap3 2
 
4.7%
erbb3 1
 
2.3%
cdkn2b 1
 
2.3%
abhd3 1
 
2.3%
rbm10 1
 
2.3%
Other values (17) 17
39.5%
2025-07-28T11:38:36.063989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 24
 
12.0%
A 19
 
9.5%
N 13
 
6.5%
E 12
 
6.0%
2 12
 
6.0%
1 12
 
6.0%
M 12
 
6.0%
R 11
 
5.5%
C 11
 
5.5%
K 9
 
4.5%
Other values (18) 65
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 24
 
12.0%
A 19
 
9.5%
N 13
 
6.5%
E 12
 
6.0%
2 12
 
6.0%
1 12
 
6.0%
M 12
 
6.0%
R 11
 
5.5%
C 11
 
5.5%
K 9
 
4.5%
Other values (18) 65
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 24
 
12.0%
A 19
 
9.5%
N 13
 
6.5%
E 12
 
6.0%
2 12
 
6.0%
1 12
 
6.0%
M 12
 
6.0%
R 11
 
5.5%
C 11
 
5.5%
K 9
 
4.5%
Other values (18) 65
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 24
 
12.0%
A 19
 
9.5%
N 13
 
6.5%
E 12
 
6.0%
2 12
 
6.0%
1 12
 
6.0%
M 12
 
6.0%
R 11
 
5.5%
C 11
 
5.5%
K 9
 
4.5%
Other values (18) 65
32.5%

Site2_Hugo_Symbol
Text

Missing 

Distinct29
Distinct (%)67.4%
Missing775
Missing (%)94.7%
Memory size6.5 KiB
2025-07-28T11:38:36.150348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length8
Mean length6.4651163
Min length3

Characters and Unicode

Total characters278
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)55.8%

Sample

1st rowNLRP6
2nd rowXRCC6BP1
3rd rowSNRPD2P2
4th rowNUP93
5th rowC11orf65
ValueCountFrequency (%)
c11orf65 6
 
14.0%
havcr1p1 6
 
14.0%
cdkn2a 3
 
7.0%
loc101929504 2
 
4.7%
braf 2
 
4.7%
nup93 1
 
2.3%
snrpd2p2 1
 
2.3%
xrcc6bp1 1
 
2.3%
clcn5 1
 
2.3%
fanca 1
 
2.3%
Other values (19) 19
44.2%
2025-07-28T11:38:36.305935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 33
 
11.9%
C 30
 
10.8%
A 20
 
7.2%
R 18
 
6.5%
P 18
 
6.5%
2 14
 
5.0%
N 11
 
4.0%
D 10
 
3.6%
5 9
 
3.2%
B 9
 
3.2%
Other values (26) 106
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 33
 
11.9%
C 30
 
10.8%
A 20
 
7.2%
R 18
 
6.5%
P 18
 
6.5%
2 14
 
5.0%
N 11
 
4.0%
D 10
 
3.6%
5 9
 
3.2%
B 9
 
3.2%
Other values (26) 106
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 33
 
11.9%
C 30
 
10.8%
A 20
 
7.2%
R 18
 
6.5%
P 18
 
6.5%
2 14
 
5.0%
N 11
 
4.0%
D 10
 
3.6%
5 9
 
3.2%
B 9
 
3.2%
Other values (26) 106
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 33
 
11.9%
C 30
 
10.8%
A 20
 
7.2%
R 18
 
6.5%
P 18
 
6.5%
2 14
 
5.0%
N 11
 
4.0%
D 10
 
3.6%
5 9
 
3.2%
B 9
 
3.2%
Other values (26) 106
38.1%

Site1_Chromosome
Categorical

High correlation  Missing 

Distinct16
Distinct (%)38.1%
Missing776
Missing (%)94.9%
Memory size6.5 KiB
20
11
9
12
6
Other values (11)
15 

Length

Max length2
Median length2
Mean length1.6666667
Min length1

Characters and Unicode

Total characters70
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)16.7%

Sample

1st row12
2nd row12
3rd row20
4th row20
5th row11

Common Values

ValueCountFrequency (%)
20 8
 
1.0%
11 7
 
0.9%
9 5
 
0.6%
12 4
 
0.5%
6 3
 
0.4%
18 2
 
0.2%
17 2
 
0.2%
14 2
 
0.2%
1 2
 
0.2%
X 1
 
0.1%
Other values (6) 6
 
0.7%
(Missing) 776
94.9%

Length

2025-07-28T11:38:36.362578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20 8
19.0%
11 7
16.7%
9 5
11.9%
12 4
9.5%
6 3
 
7.1%
18 2
 
4.8%
17 2
 
4.8%
14 2
 
4.8%
1 2
 
4.8%
x 1
 
2.4%
Other values (6) 6
14.3%

Most occurring characters

ValueCountFrequency (%)
1 29
41.4%
2 13
18.6%
0 9
 
12.9%
9 6
 
8.6%
6 3
 
4.3%
7 3
 
4.3%
4 3
 
4.3%
8 2
 
2.9%
X 1
 
1.4%
5 1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 29
41.4%
2 13
18.6%
0 9
 
12.9%
9 6
 
8.6%
6 3
 
4.3%
7 3
 
4.3%
4 3
 
4.3%
8 2
 
2.9%
X 1
 
1.4%
5 1
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 29
41.4%
2 13
18.6%
0 9
 
12.9%
9 6
 
8.6%
6 3
 
4.3%
7 3
 
4.3%
4 3
 
4.3%
8 2
 
2.9%
X 1
 
1.4%
5 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 29
41.4%
2 13
18.6%
0 9
 
12.9%
9 6
 
8.6%
6 3
 
4.3%
7 3
 
4.3%
4 3
 
4.3%
8 2
 
2.9%
X 1
 
1.4%
5 1
 
1.4%

Site2_Chromosome
Categorical

High correlation  Missing 

Distinct16
Distinct (%)38.1%
Missing776
Missing (%)94.9%
Memory size6.5 KiB
11
1
19
9
16
Other values (11)
13 

Length

Max length2
Median length2
Mean length1.5952381
Min length1

Characters and Unicode

Total characters67
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)21.4%

Sample

1st row11
2nd row12
3rd row1
4th row16
5th row11

Common Values

ValueCountFrequency (%)
11 8
 
1.0%
1 7
 
0.9%
19 7
 
0.9%
9 5
 
0.6%
16 2
 
0.2%
12 2
 
0.2%
17 2
 
0.2%
X 1
 
0.1%
14 1
 
0.1%
18 1
 
0.1%
Other values (6) 6
 
0.7%
(Missing) 776
94.9%

Length

2025-07-28T11:38:36.419583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11 8
19.0%
1 7
16.7%
19 7
16.7%
9 5
11.9%
16 2
 
4.8%
12 2
 
4.8%
17 2
 
4.8%
x 1
 
2.4%
14 1
 
2.4%
18 1
 
2.4%
Other values (6) 6
14.3%

Most occurring characters

ValueCountFrequency (%)
1 40
59.7%
9 12
 
17.9%
6 3
 
4.5%
2 3
 
4.5%
7 3
 
4.5%
4 2
 
3.0%
X 1
 
1.5%
8 1
 
1.5%
5 1
 
1.5%
0 1
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 40
59.7%
9 12
 
17.9%
6 3
 
4.5%
2 3
 
4.5%
7 3
 
4.5%
4 2
 
3.0%
X 1
 
1.5%
8 1
 
1.5%
5 1
 
1.5%
0 1
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 40
59.7%
9 12
 
17.9%
6 3
 
4.5%
2 3
 
4.5%
7 3
 
4.5%
4 2
 
3.0%
X 1
 
1.5%
8 1
 
1.5%
5 1
 
1.5%
0 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 40
59.7%
9 12
 
17.9%
6 3
 
4.5%
2 3
 
4.5%
7 3
 
4.5%
4 2
 
3.0%
X 1
 
1.5%
8 1
 
1.5%
5 1
 
1.5%
0 1
 
1.5%

Site1_Position
Real number (ℝ)

High correlation  Missing 

Distinct31
Distinct (%)73.8%
Missing776
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean66785632
Minimum3781481
Maximum1.7510272 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:36.476173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3781481
5-th percentile17230085
Q136069837
median61465466
Q31.0596775 × 108
95-th percentile1.6811026 × 108
Maximum1.7510272 × 108
Range1.7132124 × 108
Interquartile range (IQR)69897914

Descriptive statistics

Standard deviation44525268
Coefficient of variation (CV)0.66668933
Kurtosis0.21508139
Mean66785632
Median Absolute Deviation (MAD)35003134
Skewness0.8927271
Sum2.8049966 × 109
Variance1.9824995 × 1015
MonotonicityNot monotonic
2025-07-28T11:38:36.540792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
108235970 6
 
0.7%
62303901 6
 
0.7%
169564182 2
 
0.2%
62309382 1
 
0.1%
56487515 1
 
0.1%
36024780 1
 
0.1%
49420445 1
 
0.1%
47040950 1
 
0.1%
21971218 1
 
0.1%
19233543 1
 
0.1%
Other values (21) 21
 
2.6%
(Missing) 776
94.9%
ValueCountFrequency (%)
3781481 1
0.1%
5054658 1
0.1%
17124640 1
0.1%
19233543 1
0.1%
21966720 1
0.1%
21971205 1
0.1%
21971218 1
0.1%
21972058 1
0.1%
26116722 1
0.1%
26807942 1
0.1%
ValueCountFrequency (%)
175102716 1
 
0.1%
169564182 2
 
0.2%
140485658 1
 
0.1%
112341335 1
 
0.1%
108235970 6
0.7%
99163093 1
 
0.1%
89652426 1
 
0.1%
65540092 1
 
0.1%
62309382 1
 
0.1%
62303901 6
0.7%

Site2_Position
Real number (ℝ)

High correlation  Missing 

Distinct31
Distinct (%)73.8%
Missing776
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean71525713
Minimum280348
Maximum2.3158164 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:36.601408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum280348
5-th percentile12168375
Q124531369
median57093050
Q31.0825219 × 108
95-th percentile1.7490893 × 108
Maximum2.3158164 × 108
Range2.3130129 × 108
Interquartile range (IQR)83720822

Descriptive statistics

Standard deviation54481357
Coefficient of variation (CV)0.76170309
Kurtosis0.46449525
Mean71525713
Median Absolute Deviation (MAD)35102670
Skewness0.90786278
Sum3.0040799 × 109
Variance2.9682182 × 1015
MonotonicityNot monotonic
2025-07-28T11:38:36.668066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
108252191 6
 
0.7%
24531369 6
 
0.7%
117753437 2
 
0.2%
231581635 1
 
0.1%
58440275 1
 
0.1%
56779027 1
 
0.1%
280348 1
 
0.1%
49862864 1
 
0.1%
22006197 1
 
0.1%
89839561 1
 
0.1%
Other values (21) 21
 
2.6%
(Missing) 776
94.9%
ValueCountFrequency (%)
280348 1
 
0.1%
5484812 1
 
0.1%
11905315 1
 
0.1%
17166517 1
 
0.1%
21971178 1
 
0.1%
21974581 1
 
0.1%
22006179 1
 
0.1%
22006197 1
 
0.1%
24531369 6
0.7%
26197395 1
 
0.1%
ValueCountFrequency (%)
231581635 1
 
0.1%
186653690 1
 
0.1%
176175963 1
 
0.1%
150835371 1
 
0.1%
120497762 1
 
0.1%
118342820 1
 
0.1%
117753437 2
 
0.2%
115280412 1
 
0.1%
108252191 6
0.7%
89839561 1
 
0.1%

Site1_Description
Text

Missing 

Distinct31
Distinct (%)73.8%
Missing776
Missing (%)94.9%
Memory size6.5 KiB
2025-07-28T11:38:36.777158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length39
Mean length32.428571
Min length17

Characters and Unicode

Total characters1.362
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)66.7%

Sample

1st rowExon 48 of KMT2D(-)
2nd rowIntron of ERBB3(+):32bp before exon 13
3rd rowIntron of RTEL1(+):115bp before exon 11
4th rowIntron of SRC(+):66bp after exon 8
5th rowIntron of ATM(+):25bp after exon 62
ValueCountFrequency (%)
of 36
15.9%
exon 32
14.2%
intron 25
 
11.1%
before 23
 
10.2%
after 10
 
4.4%
9 9
 
4.0%
atm(+):25bp 6
 
2.7%
62 6
 
2.7%
rtel1(+):7bp 6
 
2.7%
igr 6
 
2.7%
Other values (50) 67
29.6%
2025-07-28T11:38:36.971352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184
 
13.5%
o 124
 
9.1%
n 84
 
6.2%
e 83
 
6.1%
f 71
 
5.2%
r 68
 
5.0%
b 58
 
4.3%
t 47
 
3.5%
) 42
 
3.1%
( 42
 
3.1%
Other values (44) 559
41.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
184
 
13.5%
o 124
 
9.1%
n 84
 
6.2%
e 83
 
6.1%
f 71
 
5.2%
r 68
 
5.0%
b 58
 
4.3%
t 47
 
3.5%
) 42
 
3.1%
( 42
 
3.1%
Other values (44) 559
41.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
184
 
13.5%
o 124
 
9.1%
n 84
 
6.2%
e 83
 
6.1%
f 71
 
5.2%
r 68
 
5.0%
b 58
 
4.3%
t 47
 
3.5%
) 42
 
3.1%
( 42
 
3.1%
Other values (44) 559
41.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
184
 
13.5%
o 124
 
9.1%
n 84
 
6.2%
e 83
 
6.1%
f 71
 
5.2%
r 68
 
5.0%
b 58
 
4.3%
t 47
 
3.5%
) 42
 
3.1%
( 42
 
3.1%
Other values (44) 559
41.0%

Site2_Description
Text

Missing 

Distinct30
Distinct (%)71.4%
Missing776
Missing (%)94.9%
Memory size6.5 KiB
2025-07-28T11:38:37.086856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length37
Mean length28.857143
Min length17

Characters and Unicode

Total characters1.212
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)61.9%

Sample

1st rowExon 4 of NLRP6(+)
2nd rowIGR: 105Kb before XRCC6BP1(+)
3rd rowIGR: 30Kb before SNRPD2P2(-)
4th row5-UTR of NUP93(+):3Kb before coding start
5th rowPromoter of C11orf65(-):2Kb from tx start
ValueCountFrequency (%)
of 30
 
14.9%
exon 22
 
10.9%
before 17
 
8.5%
igr 12
 
6.0%
intron 8
 
4.0%
start 7
 
3.5%
tx 6
 
3.0%
c11orf65(-):2kb 6
 
3.0%
from 6
 
3.0%
186kb 6
 
3.0%
Other values (51) 81
40.3%
2025-07-28T11:38:37.276651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
159
 
13.1%
o 104
 
8.6%
f 64
 
5.3%
r 61
 
5.0%
e 53
 
4.4%
1 52
 
4.3%
b 45
 
3.7%
( 42
 
3.5%
) 42
 
3.5%
t 40
 
3.3%
Other values (45) 550
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
159
 
13.1%
o 104
 
8.6%
f 64
 
5.3%
r 61
 
5.0%
e 53
 
4.4%
1 52
 
4.3%
b 45
 
3.7%
( 42
 
3.5%
) 42
 
3.5%
t 40
 
3.3%
Other values (45) 550
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
159
 
13.1%
o 104
 
8.6%
f 64
 
5.3%
r 61
 
5.0%
e 53
 
4.4%
1 52
 
4.3%
b 45
 
3.7%
( 42
 
3.5%
) 42
 
3.5%
t 40
 
3.3%
Other values (45) 550
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
159
 
13.1%
o 104
 
8.6%
f 64
 
5.3%
r 61
 
5.0%
e 53
 
4.4%
1 52
 
4.3%
b 45
 
3.7%
( 42
 
3.5%
) 42
 
3.5%
t 40
 
3.3%
Other values (45) 550
45.4%

NCBI_Build
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.3%
Missing775
Missing (%)94.7%
Memory size6.5 KiB
GRCh37
43 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters258
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGRCh37
2nd rowGRCh37
3rd rowGRCh37
4th rowGRCh37
5th rowGRCh37

Common Values

ValueCountFrequency (%)
GRCh37 43
 
5.3%
(Missing) 775
94.7%

Length

2025-07-28T11:38:37.331382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:37.380890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
grch37 43
100.0%

Most occurring characters

ValueCountFrequency (%)
G 43
16.7%
R 43
16.7%
C 43
16.7%
h 43
16.7%
3 43
16.7%
7 43
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 43
16.7%
R 43
16.7%
C 43
16.7%
h 43
16.7%
3 43
16.7%
7 43
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 43
16.7%
R 43
16.7%
C 43
16.7%
h 43
16.7%
3 43
16.7%
7 43
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 43
16.7%
R 43
16.7%
C 43
16.7%
h 43
16.7%
3 43
16.7%
7 43
16.7%

Class
Categorical

High correlation  Missing 

Distinct4
Distinct (%)9.5%
Missing776
Missing (%)94.9%
Memory size6.5 KiB
DELETION
21 
TRANSLOCATION
15 
INVERSION
DUPLICATION
 
2

Length

Max length13
Median length12
Mean length10.02381
Min length8

Characters and Unicode

Total characters421
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRANSLOCATION
2nd rowINVERSION
3rd rowTRANSLOCATION
4th rowTRANSLOCATION
5th rowDELETION

Common Values

ValueCountFrequency (%)
DELETION 21
 
2.6%
TRANSLOCATION 15
 
1.8%
INVERSION 4
 
0.5%
DUPLICATION 2
 
0.2%
(Missing) 776
94.9%

Length

2025-07-28T11:38:37.451585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:37.500122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
deletion 21
50.0%
translocation 15
35.7%
inversion 4
 
9.5%
duplication 2
 
4.8%

Most occurring characters

ValueCountFrequency (%)
N 61
14.5%
O 57
13.5%
T 53
12.6%
I 48
11.4%
E 46
10.9%
L 38
9.0%
A 32
7.6%
D 23
 
5.5%
R 19
 
4.5%
S 19
 
4.5%
Other values (4) 25
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61
14.5%
O 57
13.5%
T 53
12.6%
I 48
11.4%
E 46
10.9%
L 38
9.0%
A 32
7.6%
D 23
 
5.5%
R 19
 
4.5%
S 19
 
4.5%
Other values (4) 25
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61
14.5%
O 57
13.5%
T 53
12.6%
I 48
11.4%
E 46
10.9%
L 38
9.0%
A 32
7.6%
D 23
 
5.5%
R 19
 
4.5%
S 19
 
4.5%
Other values (4) 25
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61
14.5%
O 57
13.5%
T 53
12.6%
I 48
11.4%
E 46
10.9%
L 38
9.0%
A 32
7.6%
D 23
 
5.5%
R 19
 
4.5%
S 19
 
4.5%
Other values (4) 25
5.9%

Tumor_Split_Read_Count
Categorical

High correlation  Missing 

Distinct5
Distinct (%)19.2%
Missing792
Missing (%)96.8%
Memory size6.5 KiB
10.0
19 
0.0
89.0
 
1
5.0
 
1
8.0
 
1

Length

Max length4
Median length4
Mean length3.7692308
Min length3

Characters and Unicode

Total characters98
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)11.5%

Sample

1st row10.0
2nd row0.0
3rd row10.0
4th row10.0
5th row89.0

Common Values

ValueCountFrequency (%)
10.0 19
 
2.3%
0.0 4
 
0.5%
89.0 1
 
0.1%
5.0 1
 
0.1%
8.0 1
 
0.1%
(Missing) 792
96.8%

Length

2025-07-28T11:38:37.559185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:37.608684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
10.0 19
73.1%
0.0 4
 
15.4%
89.0 1
 
3.8%
5.0 1
 
3.8%
8.0 1
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 49
50.0%
. 26
26.5%
1 19
 
19.4%
8 2
 
2.0%
9 1
 
1.0%
5 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49
50.0%
. 26
26.5%
1 19
 
19.4%
8 2
 
2.0%
9 1
 
1.0%
5 1
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49
50.0%
. 26
26.5%
1 19
 
19.4%
8 2
 
2.0%
9 1
 
1.0%
5 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49
50.0%
. 26
26.5%
1 19
 
19.4%
8 2
 
2.0%
9 1
 
1.0%
5 1
 
1.0%

Tumor_Paired_End_Read_Count
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)80.8%
Missing792
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean41.846154
Minimum2
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:37.665299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.25
Q110.25
median23
Q355.5
95-th percentile113.75
Maximum158
Range156
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation40.687779
Coefficient of variation (CV)0.97231826
Kurtosis1.2906526
Mean41.846154
Median Absolute Deviation (MAD)17.5
Skewness1.3194535
Sum1088
Variance1655.4954
MonotonicityNot monotonic
2025-07-28T11:38:37.731384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 3
 
0.4%
6 2
 
0.2%
20 2
 
0.2%
51 2
 
0.2%
24 1
 
0.1%
92 1
 
0.1%
72 1
 
0.1%
57 1
 
0.1%
16 1
 
0.1%
22 1
 
0.1%
Other values (11) 11
 
1.3%
(Missing) 792
96.8%
ValueCountFrequency (%)
2 1
 
0.1%
5 1
 
0.1%
6 2
0.2%
10 3
0.4%
11 1
 
0.1%
12 1
 
0.1%
16 1
 
0.1%
20 2
0.2%
22 1
 
0.1%
24 1
 
0.1%
ValueCountFrequency (%)
158 1
0.1%
118 1
0.1%
101 1
0.1%
92 1
0.1%
86 1
0.1%
72 1
0.1%
57 1
0.1%
51 2
0.2%
50 1
0.1%
41 1
0.1%

Event_Info
Text

Missing 

Distinct27
Distinct (%)62.8%
Missing775
Missing (%)94.7%
Memory size6.5 KiB
2025-07-28T11:38:37.834409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length42
Median length16
Mean length22.744186
Min length14

Characters and Unicode

Total characters978
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)51.2%

Sample

1st rowProtein Fusion: mid-exon {KMT2D:NLRP6}
2nd rowERBB3-intergenic
3rd rowRTEL1-intergenic
4th rowAntisense Fusion
5th rowAntisense Fusion
ValueCountFrequency (%)
fusion 19
19.6%
mid-exon 10
 
10.3%
protein 9
 
9.3%
antisense 8
 
8.2%
rtel1-intergenic 7
 
7.2%
4
 
4.1%
within 3
 
3.1%
transcript 3
 
3.1%
cdkn2b:cdkn2a 2
 
2.1%
deletion 2
 
2.1%
Other values (28) 30
30.9%
2025-07-28T11:38:37.998419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 106
 
10.8%
i 102
 
10.4%
e 81
 
8.3%
63
 
6.4%
t 51
 
5.2%
o 43
 
4.4%
r 40
 
4.1%
s 38
 
3.9%
- 34
 
3.5%
A 26
 
2.7%
Other values (43) 394
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 106
 
10.8%
i 102
 
10.4%
e 81
 
8.3%
63
 
6.4%
t 51
 
5.2%
o 43
 
4.4%
r 40
 
4.1%
s 38
 
3.9%
- 34
 
3.5%
A 26
 
2.7%
Other values (43) 394
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 106
 
10.8%
i 102
 
10.4%
e 81
 
8.3%
63
 
6.4%
t 51
 
5.2%
o 43
 
4.4%
r 40
 
4.1%
s 38
 
3.9%
- 34
 
3.5%
A 26
 
2.7%
Other values (43) 394
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 106
 
10.8%
i 102
 
10.4%
e 81
 
8.3%
63
 
6.4%
t 51
 
5.2%
o 43
 
4.4%
r 40
 
4.1%
s 38
 
3.9%
- 34
 
3.5%
A 26
 
2.7%
Other values (43) 394
40.3%

Breakpoint_Type
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)4.8%
Missing776
Missing (%)94.9%
Memory size6.5 KiB
PRECISE
38 
IMPPRECISE

Length

Max length10
Median length7
Mean length7.2857143
Min length7

Characters and Unicode

Total characters306
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRECISE
2nd rowIMPPRECISE
3rd rowPRECISE
4th rowPRECISE
5th rowPRECISE

Common Values

ValueCountFrequency (%)
PRECISE 38
 
4.6%
IMPPRECISE 4
 
0.5%
(Missing) 776
94.9%

Length

2025-07-28T11:38:38.066581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:38.106470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
precise 38
90.5%
impprecise 4
 
9.5%

Most occurring characters

ValueCountFrequency (%)
E 84
27.5%
P 46
15.0%
I 46
15.0%
R 42
13.7%
C 42
13.7%
S 42
13.7%
M 4
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 84
27.5%
P 46
15.0%
I 46
15.0%
R 42
13.7%
C 42
13.7%
S 42
13.7%
M 4
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 84
27.5%
P 46
15.0%
I 46
15.0%
R 42
13.7%
C 42
13.7%
S 42
13.7%
M 4
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 84
27.5%
P 46
15.0%
I 46
15.0%
R 42
13.7%
C 42
13.7%
S 42
13.7%
M 4
 
1.3%

Connection_Type
Categorical

High correlation  Missing 

Distinct4
Distinct (%)9.5%
Missing776
Missing (%)94.9%
Memory size6.5 KiB
3to5
30 
5to5
3to3
5to3

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters168
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5to5
2nd row5to5
3rd row5to5
4th row3to3
5th row3to5

Common Values

ValueCountFrequency (%)
3to5 30
 
3.7%
5to5 4
 
0.5%
3to3 4
 
0.5%
5to3 4
 
0.5%
(Missing) 776
94.9%

Length

2025-07-28T11:38:38.168260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:38.207477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3to5 30
71.4%
5to5 4
 
9.5%
3to3 4
 
9.5%
5to3 4
 
9.5%

Most occurring characters

ValueCountFrequency (%)
3 42
25.0%
t 42
25.0%
o 42
25.0%
5 42
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 42
25.0%
t 42
25.0%
o 42
25.0%
5 42
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 42
25.0%
t 42
25.0%
o 42
25.0%
5 42
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 42
25.0%
t 42
25.0%
o 42
25.0%
5 42
25.0%

Annotation
Text

Missing 

Distinct32
Distinct (%)74.4%
Missing775
Missing (%)94.7%
Memory size6.5 KiB
2025-07-28T11:38:38.352574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length189
Median length92
Mean length79.27907
Min length50

Characters and Unicode

Total characters3.409
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)67.4%

Sample

1st rowKMT2D (NM_003482) - NLRP6 (NM_138329) rearrangement: t(11;12)(p15.5;q13.12)(chr11:g.280348::chr12:g.49420445)
2nd rowERBB3 (NM_001982) rearrangement: c.1481-33:ERBB3_chr12:g.58440275inv
3rd rowRTEL1 (NM_032957) rearrangement: t(1;20)(q42.2;q13.33)(chr1:g.231581635::chr20:g.62309382)
4th rowSRC (NM_198291) rearrangement: t(16;20)(q13;q11.23)(chr16:g.56779027::chr20:g.36024780)
5th rowATM (NM_000051) rearrangement: c.8987+25:ATM_chr11:g.108252191del
ValueCountFrequency (%)
rearrangement 42
 
19.1%
10
 
4.5%
nm_032957 7
 
3.2%
rtel1 7
 
3.2%
atm 6
 
2.7%
nm_000051 6
 
2.7%
c.8987+25:atm_chr11:g.108252191del 6
 
2.7%
t(19;20)(p13.13;q13.33)(chr19:g.24531369::chr20:g.62303901 6
 
2.7%
cdkn2a 3
 
1.4%
fusion 3
 
1.4%
Other values (110) 124
56.4%
2025-07-28T11:38:38.566304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 246
 
7.2%
0 183
 
5.4%
179
 
5.3%
r 170
 
5.0%
2 159
 
4.7%
e 156
 
4.6%
3 151
 
4.4%
: 148
 
4.3%
. 113
 
3.3%
) 97
 
2.8%
Other values (56) 1807
53.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 246
 
7.2%
0 183
 
5.4%
179
 
5.3%
r 170
 
5.0%
2 159
 
4.7%
e 156
 
4.6%
3 151
 
4.4%
: 148
 
4.3%
. 113
 
3.3%
) 97
 
2.8%
Other values (56) 1807
53.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 246
 
7.2%
0 183
 
5.4%
179
 
5.3%
r 170
 
5.0%
2 159
 
4.7%
e 156
 
4.6%
3 151
 
4.4%
: 148
 
4.3%
. 113
 
3.3%
) 97
 
2.8%
Other values (56) 1807
53.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 246
 
7.2%
0 183
 
5.4%
179
 
5.3%
r 170
 
5.0%
2 159
 
4.7%
e 156
 
4.6%
3 151
 
4.4%
: 148
 
4.3%
. 113
 
3.3%
) 97
 
2.8%
Other values (56) 1807
53.0%

DNA_Support
Boolean

Constant  Missing 

Distinct1
Distinct (%)3.7%
Missing791
Missing (%)96.7%
Memory size1.7 KiB
True
 
27
(Missing)
791 
ValueCountFrequency (%)
True 27
 
3.3%
(Missing) 791
96.7%
2025-07-28T11:38:38.606107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

RNA_Support
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.7%
Missing791
Missing (%)96.7%
Memory size6.5 KiB
unknown
27 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters189
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 27
 
3.3%
(Missing) 791
96.7%

Length

2025-07-28T11:38:38.651986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:38.686394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown 27
100.0%

Most occurring characters

ValueCountFrequency (%)
n 81
42.9%
u 27
 
14.3%
k 27
 
14.3%
o 27
 
14.3%
w 27
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 81
42.9%
u 27
 
14.3%
k 27
 
14.3%
o 27
 
14.3%
w 27
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 81
42.9%
u 27
 
14.3%
k 27
 
14.3%
o 27
 
14.3%
w 27
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 81
42.9%
u 27
 
14.3%
k 27
 
14.3%
o 27
 
14.3%
w 27
 
14.3%

SV_Length
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct23
Distinct (%)54.8%
Missing776
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean3643327.4
Minimum0
Maximum61716684
Zeros15
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:38.725146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12201.5
Q370974
95-th percentile10240049
Maximum61716684
Range61716684
Interquartile range (IQR)70974

Descriptive statistics

Standard deviation12911939
Coefficient of variation (CV)3.5439962
Kurtosis17.015202
Mean3643327.4
Median Absolute Deviation (MAD)12201.5
Skewness4.1973152
Sum1.5301975 × 108
Variance1.6671816 × 1014
MonotonicityNot monotonic
2025-07-28T11:38:38.782903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 15
 
1.8%
16221 6
 
0.7%
1952760 1
 
0.1%
2821914 1
 
0.1%
34979 1
 
0.1%
343056 1
 
0.1%
3380 1
 
0.1%
19330 1
 
0.1%
430154 1
 
0.1%
57715788 1
 
0.1%
Other values (13) 13
 
1.6%
(Missing) 776
94.9%
ValueCountFrequency (%)
0 15
1.8%
1381 1
 
0.1%
2523 1
 
0.1%
3380 1
 
0.1%
4458 1
 
0.1%
7092 1
 
0.1%
8182 1
 
0.1%
16221 6
 
0.7%
19330 1
 
0.1%
34974 1
 
0.1%
ValueCountFrequency (%)
61716684 1
0.1%
57715788 1
0.1%
10349713 1
0.1%
8156427 1
0.1%
8123834 1
0.1%
2821914 1
0.1%
1952760 1
0.1%
1073247 1
0.1%
430154 1
0.1%
343056 1
0.1%

Normal_Read_Count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct23
Distinct (%)54.8%
Missing776
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean26880.714
Minimum0
Maximum343532
Zeros15
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:38.838328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1561
Q315169.75
95-th percentile123562.6
Maximum343532
Range343532
Interquartile range (IQR)15169.75

Descriptive statistics

Standard deviation66876.692
Coefficient of variation (CV)2.4879061
Kurtosis13.844215
Mean26880.714
Median Absolute Deviation (MAD)1561
Skewness3.5888208
Sum1128990
Variance4.4724919 × 109
MonotonicityNot monotonic
2025-07-28T11:38:38.898416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 15
 
1.8%
1561 6
 
0.7%
99672 1
 
0.1%
53554 1
 
0.1%
7575 1
 
0.1%
33237 1
 
0.1%
932 1
 
0.1%
523 1
 
0.1%
30011 1
 
0.1%
343532 1
 
0.1%
Other values (13) 13
 
1.6%
(Missing) 776
94.9%
ValueCountFrequency (%)
0 15
1.8%
111 1
 
0.1%
523 1
 
0.1%
932 1
 
0.1%
1455 1
 
0.1%
1561 6
 
0.7%
1921 1
 
0.1%
4254 1
 
0.1%
6031 1
 
0.1%
7575 1
 
0.1%
ValueCountFrequency (%)
343532 1
0.1%
235499 1
0.1%
124820 1
0.1%
99672 1
0.1%
90077 1
0.1%
53554 1
0.1%
33237 1
0.1%
30011 1
0.1%
25224 1
0.1%
19853 1
0.1%

Tumor_Read_Count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct23
Distinct (%)54.8%
Missing776
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean27006.571
Minimum0
Maximum423302
Zeros15
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:38.962980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1455
Q313191.25
95-th percentile127022.25
Maximum423302
Range423302
Interquartile range (IQR)13191.25

Descriptive statistics

Standard deviation72274.962
Coefficient of variation (CV)2.6761991
Kurtosis22.701324
Mean27006.571
Median Absolute Deviation (MAD)1455
Skewness4.4317071
Sum1134276
Variance5.2236701 × 109
MonotonicityNot monotonic
2025-07-28T11:38:39.020851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 15
 
1.8%
1455 6
 
0.7%
127838 1
 
0.1%
60991 1
 
0.1%
548 1
 
0.1%
45073 1
 
0.1%
879 1
 
0.1%
191 1
 
0.1%
13673 1
 
0.1%
423302 1
 
0.1%
Other values (13) 13
 
1.6%
(Missing) 776
94.9%
ValueCountFrequency (%)
0 15
1.8%
82 1
 
0.1%
191 1
 
0.1%
548 1
 
0.1%
879 1
 
0.1%
1455 6
 
0.7%
1998 1
 
0.1%
2906 1
 
0.1%
4493 1
 
0.1%
4880 1
 
0.1%
ValueCountFrequency (%)
423302 1
0.1%
148681 1
0.1%
127838 1
0.1%
111523 1
0.1%
80408 1
0.1%
60991 1
0.1%
45073 1
0.1%
31677 1
0.1%
26712 1
0.1%
16551 1
0.1%

Normal_Variant_Count
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.4%
Missing776
Missing (%)94.9%
Memory size6.5 KiB
0.0
42 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42
 
5.1%
(Missing) 776
94.9%

Length

2025-07-28T11:38:39.228319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:39.261363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42
100.0%

Most occurring characters

ValueCountFrequency (%)
0 84
66.7%
. 42
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 84
66.7%
. 42
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 84
66.7%
. 42
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 84
66.7%
. 42
33.3%

Tumor_Variant_Count
Real number (ℝ)

High correlation  Missing 

Distinct24
Distinct (%)57.1%
Missing776
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean60.071429
Minimum2
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:39.298419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q113
median50
Q386.25
95-th percentile160
Maximum208
Range206
Interquartile range (IQR)73.25

Descriptive statistics

Standard deviation56.711247
Coefficient of variation (CV)0.94406356
Kurtosis0.050579506
Mean60.071429
Median Absolute Deviation (MAD)38.5
Skewness1.0740502
Sum2523
Variance3216.1655
MonotonicityNot monotonic
2025-07-28T11:38:39.362277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
160 6
 
0.7%
50 6
 
0.7%
9 4
 
0.5%
62 2
 
0.2%
6 2
 
0.2%
20 2
 
0.2%
16 2
 
0.2%
91 2
 
0.2%
208 1
 
0.1%
29 1
 
0.1%
Other values (14) 14
 
1.7%
(Missing) 776
94.9%
ValueCountFrequency (%)
2 1
 
0.1%
5 1
 
0.1%
6 2
0.2%
9 4
0.5%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
16 2
0.2%
20 2
0.2%
21 1
 
0.1%
ValueCountFrequency (%)
208 1
 
0.1%
160 6
0.7%
128 1
 
0.1%
100 1
 
0.1%
91 2
 
0.2%
72 1
 
0.1%
62 2
 
0.2%
61 1
 
0.1%
60 1
 
0.1%
54 1
 
0.1%

Comments
Text

Missing 

Distinct32
Distinct (%)74.4%
Missing775
Missing (%)94.7%
Memory size6.5 KiB
2025-07-28T11:38:39.501184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length553
Median length162
Mean length142.16279
Min length76

Characters and Unicode

Total characters6.113
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)67.4%

Sample

1st rowNote: The KMT2D - NLRP6 rearrangement is a translocation that results in a fusion of KMT2D exons 1 - 48 to NLRP6 exons 4 - 8. The breakpoints are within KMT2D exon 48 and NLRP6 exon 4. Functional significance is undetermined.
2nd rowNote: The ERBB3 rearrangement is an inversion of exons 13 - 28. The rearrangement includes the kinase domain of ERBB3. Functional significance is undetermined.
3rd rowNote: The RTEL1 rearrangement is a translocation with a breakpoint in intron 10. Functional significance is undetermined.
4th rowNote: The SRC rearrangement is a translocation with a breakpoint in intron 8. Functional significance is undetermined.
5th rowNote: The ATM rearrangement is a deletion of exons 63. its functional significance is undetermined.
ValueCountFrequency (%)
is 85
 
8.9%
the 75
 
7.9%
of 47
 
4.9%
a 45
 
4.7%
rearrangement 44
 
4.6%
note 42
 
4.4%
functional 34
 
3.6%
undetermined 34
 
3.6%
significance 34
 
3.6%
exon 33
 
3.5%
Other values (142) 481
50.4%
2025-07-28T11:38:39.706562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
915
15.0%
e 601
 
9.8%
n 557
 
9.1%
i 455
 
7.4%
t 357
 
5.8%
a 317
 
5.2%
o 315
 
5.2%
s 262
 
4.3%
r 258
 
4.2%
c 138
 
2.3%
Other values (55) 1938
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
915
15.0%
e 601
 
9.8%
n 557
 
9.1%
i 455
 
7.4%
t 357
 
5.8%
a 317
 
5.2%
o 315
 
5.2%
s 262
 
4.3%
r 258
 
4.2%
c 138
 
2.3%
Other values (55) 1938
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
915
15.0%
e 601
 
9.8%
n 557
 
9.1%
i 455
 
7.4%
t 357
 
5.8%
a 317
 
5.2%
o 315
 
5.2%
s 262
 
4.3%
r 258
 
4.2%
c 138
 
2.3%
Other values (55) 1938
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
915
15.0%
e 601
 
9.8%
n 557
 
9.1%
i 455
 
7.4%
t 357
 
5.8%
a 317
 
5.2%
o 315
 
5.2%
s 262
 
4.3%
r 258
 
4.2%
c 138
 
2.3%
Other values (55) 1938
31.7%

cancer_type
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Gastrointestinal Stromal Tumor
818 

Length

Max length30
Median length30
Mean length30
Min length30

Characters and Unicode

Total characters24.540
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGastrointestinal Stromal Tumor
2nd rowGastrointestinal Stromal Tumor
3rd rowGastrointestinal Stromal Tumor
4th rowGastrointestinal Stromal Tumor
5th rowGastrointestinal Stromal Tumor

Common Values

ValueCountFrequency (%)
Gastrointestinal Stromal Tumor 818
100.0%

Length

2025-07-28T11:38:39.759001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:39.796548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gastrointestinal 818
33.3%
stromal 818
33.3%
tumor 818
33.3%

Most occurring characters

ValueCountFrequency (%)
t 3272
13.3%
r 2454
10.0%
a 2454
10.0%
o 2454
10.0%
s 1636
 
6.7%
i 1636
 
6.7%
n 1636
 
6.7%
1636
 
6.7%
l 1636
 
6.7%
m 1636
 
6.7%
Other values (5) 4090
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3272
13.3%
r 2454
10.0%
a 2454
10.0%
o 2454
10.0%
s 1636
 
6.7%
i 1636
 
6.7%
n 1636
 
6.7%
1636
 
6.7%
l 1636
 
6.7%
m 1636
 
6.7%
Other values (5) 4090
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3272
13.3%
r 2454
10.0%
a 2454
10.0%
o 2454
10.0%
s 1636
 
6.7%
i 1636
 
6.7%
n 1636
 
6.7%
1636
 
6.7%
l 1636
 
6.7%
m 1636
 
6.7%
Other values (5) 4090
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3272
13.3%
r 2454
10.0%
a 2454
10.0%
o 2454
10.0%
s 1636
 
6.7%
i 1636
 
6.7%
n 1636
 
6.7%
1636
 
6.7%
l 1636
 
6.7%
m 1636
 
6.7%
Other values (5) 4090
16.7%

sample_type
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Primary
473 
Metastasis
333 
Local Recurrence
 
9
Unknown
 
3

Length

Max length16
Median length7
Mean length8.3202934
Min length7

Characters and Unicode

Total characters6.806
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimary
2nd rowPrimary
3rd rowPrimary
4th rowPrimary
5th rowMetastasis

Common Values

ValueCountFrequency (%)
Primary 473
57.8%
Metastasis 333
40.7%
Local Recurrence 9
 
1.1%
Unknown 3
 
0.4%

Length

2025-07-28T11:38:39.845008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:39.885297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
primary 473
57.2%
metastasis 333
40.3%
local 9
 
1.1%
recurrence 9
 
1.1%
unknown 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 1148
16.9%
s 999
14.7%
r 964
14.2%
i 806
11.8%
t 666
9.8%
m 473
6.9%
P 473
6.9%
y 473
6.9%
e 360
 
5.3%
M 333
 
4.9%
Other values (11) 111
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1148
16.9%
s 999
14.7%
r 964
14.2%
i 806
11.8%
t 666
9.8%
m 473
6.9%
P 473
6.9%
y 473
6.9%
e 360
 
5.3%
M 333
 
4.9%
Other values (11) 111
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1148
16.9%
s 999
14.7%
r 964
14.2%
i 806
11.8%
t 666
9.8%
m 473
6.9%
P 473
6.9%
y 473
6.9%
e 360
 
5.3%
M 333
 
4.9%
Other values (11) 111
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1148
16.9%
s 999
14.7%
r 964
14.2%
i 806
11.8%
t 666
9.8%
m 473
6.9%
P 473
6.9%
y 473
6.9%
e 360
 
5.3%
M 333
 
4.9%
Other values (11) 111
 
1.6%

sample_class
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Tumor
818 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4.090
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTumor
2nd rowTumor
3rd rowTumor
4th rowTumor
5th rowTumor

Common Values

ValueCountFrequency (%)
Tumor 818
100.0%

Length

2025-07-28T11:38:39.941744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:39.973847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tumor 818
100.0%

Most occurring characters

ValueCountFrequency (%)
T 818
20.0%
u 818
20.0%
m 818
20.0%
o 818
20.0%
r 818
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 818
20.0%
u 818
20.0%
m 818
20.0%
o 818
20.0%
r 818
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 818
20.0%
u 818
20.0%
m 818
20.0%
o 818
20.0%
r 818
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 818
20.0%
u 818
20.0%
m 818
20.0%
o 818
20.0%
r 818
20.0%

metastatic_site
Categorical

High correlation  Imbalance 

Distinct32
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Not Applicable
495 
Liver
154 
Peritoneum
 
18
Mesentery
 
15
Abdomen
 
13
Other values (27)
123 

Length

Max length18
Median length14
Mean length11.169927
Min length4

Characters and Unicode

Total characters9.137
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowLiver

Common Values

ValueCountFrequency (%)
Not Applicable 495
60.5%
Liver 154
 
18.8%
Peritoneum 18
 
2.2%
Mesentery 15
 
1.8%
Abdomen 13
 
1.6%
Pelvis 13
 
1.6%
Small Bowel 13
 
1.6%
Skin 11
 
1.3%
Omentum 11
 
1.3%
Spleen 9
 
1.1%
Other values (22) 66
 
8.1%

Length

2025-07-28T11:38:40.026978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
not 498
36.5%
applicable 495
36.3%
liver 154
 
11.3%
peritoneum 18
 
1.3%
mesentery 15
 
1.1%
pelvis 15
 
1.1%
abdomen 13
 
1.0%
small 13
 
1.0%
bowel 13
 
1.0%
skin 11
 
0.8%
Other values (29) 119
 
8.7%

Most occurring characters

ValueCountFrequency (%)
l 1105
12.1%
p 1011
11.1%
e 847
 
9.3%
i 723
 
7.9%
o 587
 
6.4%
a 565
 
6.2%
t 562
 
6.2%
546
 
6.0%
b 519
 
5.7%
A 516
 
5.6%
Other values (27) 2156
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1105
12.1%
p 1011
11.1%
e 847
 
9.3%
i 723
 
7.9%
o 587
 
6.4%
a 565
 
6.2%
t 562
 
6.2%
546
 
6.0%
b 519
 
5.7%
A 516
 
5.6%
Other values (27) 2156
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1105
12.1%
p 1011
11.1%
e 847
 
9.3%
i 723
 
7.9%
o 587
 
6.4%
a 565
 
6.2%
t 562
 
6.2%
546
 
6.0%
b 519
 
5.7%
A 516
 
5.6%
Other values (27) 2156
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1105
12.1%
p 1011
11.1%
e 847
 
9.3%
i 723
 
7.9%
o 587
 
6.4%
a 565
 
6.2%
t 562
 
6.2%
546
 
6.0%
b 519
 
5.7%
A 516
 
5.6%
Other values (27) 2156
23.6%

primary_site
Categorical

Distinct24
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Stomach
256 
Small Bowel
242 
Soft Tissue
96 
Gastric
75 
Intraabdominal
26 
Other values (19)
123 

Length

Max length25
Median length16
Mean length9.1845966
Min length4

Characters and Unicode

Total characters7.513
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)1.1%

Sample

1st rowGastric
2nd rowGastric
3rd rowGastric
4th rowGastric
5th rowStomach

Common Values

ValueCountFrequency (%)
Stomach 256
31.3%
Small Bowel 242
29.6%
Soft Tissue 96
 
11.7%
Gastric 75
 
9.2%
Intraabdominal 26
 
3.2%
Small Intestine 26
 
3.2%
Duodenum 21
 
2.6%
Rectum 17
 
2.1%
Abdomen 15
 
1.8%
Bowel 12
 
1.5%
Other values (14) 32
 
3.9%

Length

2025-07-28T11:38:40.099120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
small 268
22.4%
stomach 256
21.4%
bowel 254
21.3%
soft 96
 
8.0%
tissue 96
 
8.0%
gastric 79
 
6.6%
intraabdominal 26
 
2.2%
intestine 26
 
2.2%
duodenum 21
 
1.8%
rectum 18
 
1.5%
Other values (20) 54
 
4.5%

Most occurring characters

ValueCountFrequency (%)
l 839
11.2%
a 701
 
9.3%
o 696
 
9.3%
S 620
 
8.3%
m 611
 
8.1%
t 531
 
7.1%
e 472
 
6.3%
376
 
5.0%
c 358
 
4.8%
s 319
 
4.2%
Other values (29) 1990
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 839
11.2%
a 701
 
9.3%
o 696
 
9.3%
S 620
 
8.3%
m 611
 
8.1%
t 531
 
7.1%
e 472
 
6.3%
376
 
5.0%
c 358
 
4.8%
s 319
 
4.2%
Other values (29) 1990
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 839
11.2%
a 701
 
9.3%
o 696
 
9.3%
S 620
 
8.3%
m 611
 
8.1%
t 531
 
7.1%
e 472
 
6.3%
376
 
5.0%
c 358
 
4.8%
s 319
 
4.2%
Other values (29) 1990
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 839
11.2%
a 701
 
9.3%
o 696
 
9.3%
S 620
 
8.3%
m 611
 
8.1%
t 531
 
7.1%
e 472
 
6.3%
376
 
5.0%
c 358
 
4.8%
s 319
 
4.2%
Other values (29) 1990
26.5%

cancer_type_detailed
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Gastrointestinal Stromal Tumor
818 

Length

Max length30
Median length30
Mean length30
Min length30

Characters and Unicode

Total characters24.540
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGastrointestinal Stromal Tumor
2nd rowGastrointestinal Stromal Tumor
3rd rowGastrointestinal Stromal Tumor
4th rowGastrointestinal Stromal Tumor
5th rowGastrointestinal Stromal Tumor

Common Values

ValueCountFrequency (%)
Gastrointestinal Stromal Tumor 818
100.0%

Length

2025-07-28T11:38:40.158109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:40.199149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gastrointestinal 818
33.3%
stromal 818
33.3%
tumor 818
33.3%

Most occurring characters

ValueCountFrequency (%)
t 3272
13.3%
r 2454
10.0%
a 2454
10.0%
o 2454
10.0%
s 1636
 
6.7%
i 1636
 
6.7%
n 1636
 
6.7%
1636
 
6.7%
l 1636
 
6.7%
m 1636
 
6.7%
Other values (5) 4090
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3272
13.3%
r 2454
10.0%
a 2454
10.0%
o 2454
10.0%
s 1636
 
6.7%
i 1636
 
6.7%
n 1636
 
6.7%
1636
 
6.7%
l 1636
 
6.7%
m 1636
 
6.7%
Other values (5) 4090
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3272
13.3%
r 2454
10.0%
a 2454
10.0%
o 2454
10.0%
s 1636
 
6.7%
i 1636
 
6.7%
n 1636
 
6.7%
1636
 
6.7%
l 1636
 
6.7%
m 1636
 
6.7%
Other values (5) 4090
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3272
13.3%
r 2454
10.0%
a 2454
10.0%
o 2454
10.0%
s 1636
 
6.7%
i 1636
 
6.7%
n 1636
 
6.7%
1636
 
6.7%
l 1636
 
6.7%
m 1636
 
6.7%
Other values (5) 4090
16.7%

gene_panel
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
IMPACT468
392 
IMPACT410
260 
IMPACT505
91 
IMPACT341
75 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters7.362
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIMPACT341
2nd rowIMPACT341
3rd rowIMPACT341
4th rowIMPACT341
5th rowIMPACT341

Common Values

ValueCountFrequency (%)
IMPACT468 392
47.9%
IMPACT410 260
31.8%
IMPACT505 91
 
11.1%
IMPACT341 75
 
9.2%

Length

2025-07-28T11:38:40.243562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:40.294202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
impact468 392
47.9%
impact410 260
31.8%
impact505 91
 
11.1%
impact341 75
 
9.2%

Most occurring characters

ValueCountFrequency (%)
I 818
11.1%
M 818
11.1%
P 818
11.1%
A 818
11.1%
C 818
11.1%
T 818
11.1%
4 727
9.9%
6 392
5.3%
8 392
5.3%
0 351
4.8%
Other values (3) 592
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 818
11.1%
M 818
11.1%
P 818
11.1%
A 818
11.1%
C 818
11.1%
T 818
11.1%
4 727
9.9%
6 392
5.3%
8 392
5.3%
0 351
4.8%
Other values (3) 592
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 818
11.1%
M 818
11.1%
P 818
11.1%
A 818
11.1%
C 818
11.1%
T 818
11.1%
4 727
9.9%
6 392
5.3%
8 392
5.3%
0 351
4.8%
Other values (3) 592
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 818
11.1%
M 818
11.1%
P 818
11.1%
A 818
11.1%
C 818
11.1%
T 818
11.1%
4 727
9.9%
6 392
5.3%
8 392
5.3%
0 351
4.8%
Other values (3) 592
8.0%

sample_coverage
Real number (ℝ)

High correlation 

Distinct406
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean673.23472
Minimum106
Maximum1270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:40.395860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile326
Q1521.25
median674
Q3813
95-th percentile1023
Maximum1270
Range1164
Interquartile range (IQR)291.75

Descriptive statistics

Standard deviation212.73839
Coefficient of variation (CV)0.31599438
Kurtosis-0.2469397
Mean673.23472
Median Absolute Deviation (MAD)146
Skewness0.030061362
Sum550706
Variance45257.622
MonotonicityNot monotonic
2025-07-28T11:38:40.502358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
677 13
 
1.6%
1132 12
 
1.5%
1023 11
 
1.3%
674 9
 
1.1%
747 8
 
1.0%
808 7
 
0.9%
780 7
 
0.9%
682 7
 
0.9%
694 7
 
0.9%
434 7
 
0.9%
Other values (396) 730
89.2%
ValueCountFrequency (%)
106 2
0.2%
148 1
 
0.1%
152 1
 
0.1%
172 2
0.2%
176 2
0.2%
182 4
0.5%
184 1
 
0.1%
189 1
 
0.1%
205 1
 
0.1%
206 1
 
0.1%
ValueCountFrequency (%)
1270 1
 
0.1%
1243 1
 
0.1%
1225 1
 
0.1%
1152 1
 
0.1%
1135 1
 
0.1%
1132 12
1.5%
1108 1
 
0.1%
1107 2
 
0.2%
1085 5
0.6%
1080 1
 
0.1%

tumor_purity
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)1.5%
Missing26
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean66.224747
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:40.581124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q160
median70
Q380
95-th percentile90
Maximum90
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.355666
Coefficient of variation (CV)0.2771723
Kurtosis0.31816111
Mean66.224747
Median Absolute Deviation (MAD)10
Skewness-0.87996749
Sum52450
Variance336.93046
MonotonicityNot monotonic
2025-07-28T11:38:40.638128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
80 202
24.7%
70 162
19.8%
60 151
18.5%
90 98
12.0%
50 63
 
7.7%
40 42
 
5.1%
30 41
 
5.0%
20 12
 
1.5%
10 9
 
1.1%
85 6
 
0.7%
Other values (2) 6
 
0.7%
(Missing) 26
 
3.2%
ValueCountFrequency (%)
10 9
 
1.1%
15 2
 
0.2%
20 12
 
1.5%
30 41
 
5.0%
35 4
 
0.5%
40 42
 
5.1%
50 63
 
7.7%
60 151
18.5%
70 162
19.8%
80 202
24.7%
ValueCountFrequency (%)
90 98
12.0%
85 6
 
0.7%
80 202
24.7%
70 162
19.8%
60 151
18.5%
50 63
 
7.7%
40 42
 
5.1%
35 4
 
0.5%
30 41
 
5.0%
20 12
 
1.5%

oncotree_code
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
GIST
818 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3.272
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGIST
2nd rowGIST
3rd rowGIST
4th rowGIST
5th rowGIST

Common Values

ValueCountFrequency (%)
GIST 818
100.0%

Length

2025-07-28T11:38:40.702130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:40.736103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gist 818
100.0%

Most occurring characters

ValueCountFrequency (%)
G 818
25.0%
I 818
25.0%
S 818
25.0%
T 818
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 818
25.0%
I 818
25.0%
S 818
25.0%
T 818
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 818
25.0%
I 818
25.0%
S 818
25.0%
T 818
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 818
25.0%
I 818
25.0%
S 818
25.0%
T 818
25.0%

msi_score
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct203
Distinct (%)25.1%
Missing9
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean0.92142151
Minimum0
Maximum16.06
Zeros121
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:40.788196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.11
median0.56
Q31.3
95-th percentile2.646
Maximum16.06
Range16.06
Interquartile range (IQR)1.19

Descriptive statistics

Standard deviation1.1746473
Coefficient of variation (CV)1.2748208
Kurtosis38.465651
Mean0.92142151
Median Absolute Deviation (MAD)0.5
Skewness4.2278325
Sum745.43
Variance1.3797964
MonotonicityNot monotonic
2025-07-28T11:38:40.874247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 121
 
14.8%
0.08 17
 
2.1%
0.09 16
 
2.0%
0.07 13
 
1.6%
1.53 12
 
1.5%
0.17 12
 
1.5%
2.33 12
 
1.5%
0.06 12
 
1.5%
2.46 11
 
1.3%
0.3 11
 
1.3%
Other values (193) 572
69.9%
ValueCountFrequency (%)
0 121
14.8%
0.02 2
 
0.2%
0.05 5
 
0.6%
0.06 12
 
1.5%
0.07 13
 
1.6%
0.08 17
 
2.1%
0.09 16
 
2.0%
0.1 9
 
1.1%
0.11 8
 
1.0%
0.12 5
 
0.6%
ValueCountFrequency (%)
16.06 1
 
0.1%
8.33 1
 
0.1%
7.92 1
 
0.1%
6.1 1
 
0.1%
5.53 2
0.2%
5.5 1
 
0.1%
5.07 4
0.5%
4.93 1
 
0.1%
4.57 1
 
0.1%
4.55 1
 
0.1%

msi_type
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.4%
Missing9
Missing (%)1.1%
Memory size6.5 KiB
Stable
716 
Do not report
 
67
Indeterminate
 
26

Length

Max length13
Median length6
Mean length6.8046972
Min length6

Characters and Unicode

Total characters5.505
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndeterminate
2nd rowIndeterminate
3rd rowIndeterminate
4th rowIndeterminate
5th rowIndeterminate

Common Values

ValueCountFrequency (%)
Stable 716
87.5%
Do not report 67
 
8.2%
Indeterminate 26
 
3.2%
(Missing) 9
 
1.1%

Length

2025-07-28T11:38:40.952085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:40.997437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
stable 716
75.9%
do 67
 
7.1%
not 67
 
7.1%
report 67
 
7.1%
indeterminate 26
 
2.8%

Most occurring characters

ValueCountFrequency (%)
t 902
16.4%
e 861
15.6%
a 742
13.5%
S 716
13.0%
b 716
13.0%
l 716
13.0%
o 201
 
3.7%
r 160
 
2.9%
134
 
2.4%
n 119
 
2.2%
Other values (6) 238
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 902
16.4%
e 861
15.6%
a 742
13.5%
S 716
13.0%
b 716
13.0%
l 716
13.0%
o 201
 
3.7%
r 160
 
2.9%
134
 
2.4%
n 119
 
2.2%
Other values (6) 238
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 902
16.4%
e 861
15.6%
a 742
13.5%
S 716
13.0%
b 716
13.0%
l 716
13.0%
o 201
 
3.7%
r 160
 
2.9%
134
 
2.4%
n 119
 
2.2%
Other values (6) 238
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 902
16.4%
e 861
15.6%
a 742
13.5%
S 716
13.0%
b 716
13.0%
l 716
13.0%
o 201
 
3.7%
r 160
 
2.9%
134
 
2.4%
n 119
 
2.2%
Other values (6) 238
 
4.3%

somatic_status
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Matched
818 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5.726
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMatched
2nd rowMatched
3rd rowMatched
4th rowMatched
5th rowMatched

Common Values

ValueCountFrequency (%)
Matched 818
100.0%

Length

2025-07-28T11:38:41.047578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:41.079675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
matched 818
100.0%

Most occurring characters

ValueCountFrequency (%)
M 818
14.3%
a 818
14.3%
t 818
14.3%
c 818
14.3%
h 818
14.3%
e 818
14.3%
d 818
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 818
14.3%
a 818
14.3%
t 818
14.3%
c 818
14.3%
h 818
14.3%
e 818
14.3%
d 818
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 818
14.3%
a 818
14.3%
t 818
14.3%
c 818
14.3%
h 818
14.3%
e 818
14.3%
d 818
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 818
14.3%
a 818
14.3%
t 818
14.3%
c 818
14.3%
h 818
14.3%
e 818
14.3%
d 818
14.3%
Distinct117
Distinct (%)14.5%
Missing10
Missing (%)1.2%
Memory size6.5 KiB
2025-07-28T11:38:41.219763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3341584
Min length2

Characters and Unicode

Total characters1.886
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)3.6%

Sample

1st row69.0
2nd row69.0
3rd row69.0
4th row69.0
5th row70
ValueCountFrequency (%)
54 33
 
4.1%
61 29
 
3.6%
60 26
 
3.2%
63 26
 
3.2%
53 25
 
3.1%
58 24
 
3.0%
64 22
 
2.7%
48 21
 
2.6%
66 21
 
2.6%
49 20
 
2.5%
Other values (106) 561
69.4%
2025-07-28T11:38:41.496688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 323
17.1%
5 257
13.6%
0 218
11.6%
7 209
11.1%
4 188
10.0%
3 160
8.5%
8 144
7.6%
. 134
7.1%
2 91
 
4.8%
9 82
 
4.3%
Other values (2) 80
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 323
17.1%
5 257
13.6%
0 218
11.6%
7 209
11.1%
4 188
10.0%
3 160
8.5%
8 144
7.6%
. 134
7.1%
2 91
 
4.8%
9 82
 
4.3%
Other values (2) 80
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 323
17.1%
5 257
13.6%
0 218
11.6%
7 209
11.1%
4 188
10.0%
3 160
8.5%
8 144
7.6%
. 134
7.1%
2 91
 
4.8%
9 82
 
4.3%
Other values (2) 80
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 323
17.1%
5 257
13.6%
0 218
11.6%
7 209
11.1%
4 188
10.0%
3 160
8.5%
8 144
7.6%
. 134
7.1%
2 91
 
4.8%
9 82
 
4.3%
Other values (2) 80
 
4.2%

tmb_nonsynonymous
Real number (ℝ)

High correlation  Zeros 

Distinct34
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.761925
Minimum0
Maximum17.746485
Zeros26
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:41.570069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.033333333
Q10.8646981
median1.7293962
Q32.5940943
95-th percentile4.8935985
Maximum17.746485
Range17.746485
Interquartile range (IQR)1.7293962

Descriptive statistics

Standard deviation1.598013
Coefficient of variation (CV)0.90696995
Kurtosis17.087308
Mean1.761925
Median Absolute Deviation (MAD)0.8646981
Skewness2.5723131
Sum1441.2547
Variance2.5536457
MonotonicityNot monotonic
2025-07-28T11:38:41.641051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.864698095 117
14.3%
1.72939619 95
 
11.6%
0.978719698 64
 
7.8%
1.957439395 58
 
7.1%
2.594094285 52
 
6.4%
2.936159093 51
 
6.2%
0.03333333333 44
 
5.4%
0.1 32
 
3.9%
2.218310601 30
 
3.7%
0.820347237 28
 
3.4%
Other values (24) 247
30.2%
ValueCountFrequency (%)
0 26
3.2%
0.03333333333 44
5.4%
0.06666666667 22
2.7%
0.1 32
3.9%
0.1333333333 20
2.4%
0.1666666667 1
 
0.1%
0.2 13
 
1.6%
0.2333333333 2
 
0.2%
0.3 1
 
0.1%
0.5333333333 1
 
0.1%
ValueCountFrequency (%)
17.74648481 1
 
0.1%
14.69986761 1
 
0.1%
7.782282855 1
 
0.1%
6.052886665 4
 
0.5%
5.872318186 17
2.1%
5.18818857 11
1.3%
4.893598488 9
1.1%
4.436621202 1
 
0.1%
4.323490475 22
2.7%
3.91487879 13
1.6%

genes_mutados
Text

Missing 

Distinct277
Distinct (%)35.0%
Missing26
Missing (%)3.2%
Memory size6.5 KiB
2025-07-28T11:38:41.828219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length144
Median length119
Mean length16.837121
Min length7

Characters and Unicode

Total characters13.335
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)25.0%

Sample

1st row['KIT', 'RB1', 'TP53']
2nd row['KIT', 'RB1', 'TP53']
3rd row['KIT', 'RB1', 'TP53']
4th row['KIT', 'RB1', 'TP53']
5th row['RB1', 'TP53']
ValueCountFrequency (%)
kit 602
36.0%
pdgfra 71
 
4.3%
rb1 55
 
3.3%
tp53 48
 
2.9%
nf1 45
 
2.7%
max 41
 
2.5%
setd2 38
 
2.3%
mga 33
 
2.0%
pten 27
 
1.6%
tsc2 24
 
1.4%
Other values (220) 686
41.1%
2025-07-28T11:38:42.115210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 3340
25.0%
T 991
 
7.4%
878
 
6.6%
, 878
 
6.6%
[ 792
 
5.9%
] 792
 
5.9%
K 751
 
5.6%
I 685
 
5.1%
A 392
 
2.9%
R 388
 
2.9%
Other values (32) 3448
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 3340
25.0%
T 991
 
7.4%
878
 
6.6%
, 878
 
6.6%
[ 792
 
5.9%
] 792
 
5.9%
K 751
 
5.6%
I 685
 
5.1%
A 392
 
2.9%
R 388
 
2.9%
Other values (32) 3448
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 3340
25.0%
T 991
 
7.4%
878
 
6.6%
, 878
 
6.6%
[ 792
 
5.9%
] 792
 
5.9%
K 751
 
5.6%
I 685
 
5.1%
A 392
 
2.9%
R 388
 
2.9%
Other values (32) 3448
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 3340
25.0%
T 991
 
7.4%
878
 
6.6%
, 878
 
6.6%
[ 792
 
5.9%
] 792
 
5.9%
K 751
 
5.6%
I 685
 
5.1%
A 392
 
2.9%
R 388
 
2.9%
Other values (32) 3448
25.9%

race
Categorical

High correlation  Imbalance  Missing 

Distinct7
Distinct (%)0.9%
Missing10
Missing (%)1.2%
Memory size6.5 KiB
WHITE
603 
BLACK OR AFRICAN AMERICAN
98 
ASIAN-FAR EAST/INDIAN SUBCONT
 
55
PT REFUSED TO ANSWER
 
29
OTHER
 
15
Other values (2)
 
8

Length

Max length29
Median length5
Mean length9.644802
Min length5

Characters and Unicode

Total characters7.793
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowWHITE
2nd rowWHITE
3rd rowWHITE
4th rowWHITE
5th rowWHITE

Common Values

ValueCountFrequency (%)
WHITE 603
73.7%
BLACK OR AFRICAN AMERICAN 98
 
12.0%
ASIAN-FAR EAST/INDIAN SUBCONT 55
 
6.7%
PT REFUSED TO ANSWER 29
 
3.5%
OTHER 15
 
1.8%
UNKNOWN 7
 
0.9%
NATIVE AMERICAN-AM IND/ALASKA 1
 
0.1%
(Missing) 10
 
1.2%

Length

2025-07-28T11:38:42.176831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:42.227379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
white 603
46.3%
black 98
 
7.5%
or 98
 
7.5%
african 98
 
7.5%
american 98
 
7.5%
asian-far 55
 
4.2%
east/indian 55
 
4.2%
subcont 55
 
4.2%
pt 29
 
2.2%
refused 29
 
2.2%
Other values (7) 83
 
6.4%

Most occurring characters

ValueCountFrequency (%)
I 967
12.4%
E 860
11.0%
A 801
10.3%
T 787
10.1%
W 639
8.2%
H 618
7.9%
493
 
6.3%
N 469
 
6.0%
R 423
 
5.4%
C 350
 
4.5%
Other values (13) 1386
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 967
12.4%
E 860
11.0%
A 801
10.3%
T 787
10.1%
W 639
8.2%
H 618
7.9%
493
 
6.3%
N 469
 
6.0%
R 423
 
5.4%
C 350
 
4.5%
Other values (13) 1386
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 967
12.4%
E 860
11.0%
A 801
10.3%
T 787
10.1%
W 639
8.2%
H 618
7.9%
493
 
6.3%
N 469
 
6.0%
R 423
 
5.4%
C 350
 
4.5%
Other values (13) 1386
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 967
12.4%
E 860
11.0%
A 801
10.3%
T 787
10.1%
W 639
8.2%
H 618
7.9%
493
 
6.3%
N 469
 
6.0%
R 423
 
5.4%
C 350
 
4.5%
Other values (13) 1386
17.8%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing5
Missing (%)0.6%
Memory size6.5 KiB
Male
458 
Female
355 

Length

Max length6
Median length4
Mean length4.8733087
Min length4

Characters and Unicode

Total characters3.962
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 458
56.0%
Female 355
43.4%
(Missing) 5
 
0.6%

Length

2025-07-28T11:38:42.298546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:42.355437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 458
56.3%
female 355
43.7%

Most occurring characters

ValueCountFrequency (%)
e 1168
29.5%
a 813
20.5%
l 813
20.5%
M 458
 
11.6%
F 355
 
9.0%
m 355
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1168
29.5%
a 813
20.5%
l 813
20.5%
M 458
 
11.6%
F 355
 
9.0%
m 355
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1168
29.5%
a 813
20.5%
l 813
20.5%
M 458
 
11.6%
F 355
 
9.0%
m 355
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1168
29.5%
a 813
20.5%
l 813
20.5%
M 458
 
11.6%
F 355
 
9.0%
m 355
 
9.0%

ethnicity
Categorical

Imbalance  Missing 

Distinct7
Distinct (%)0.9%
Missing20
Missing (%)2.4%
Memory size6.5 KiB
Non-Spanish; Non-Hispanic
728 
Spanish NOS; Hispanic NOS, Latino NOS
 
38
Unknown whether Spanish or not
 
27
Puerto Rican
 
2
Dominican Republic
 
1
Other values (2)
 
2

Length

Max length38
Median length25
Mean length25.736842
Min length5

Characters and Unicode

Total characters20.538
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st rowNon-Spanish; Non-Hispanic
2nd rowNon-Spanish; Non-Hispanic
3rd rowNon-Spanish; Non-Hispanic
4th rowNon-Spanish; Non-Hispanic
5th rowNon-Spanish; Non-Hispanic

Common Values

ValueCountFrequency (%)
Non-Spanish; Non-Hispanic 728
89.0%
Spanish NOS; Hispanic NOS, Latino NOS 38
 
4.6%
Unknown whether Spanish or not 27
 
3.3%
Puerto Rican 2
 
0.2%
Dominican Republic 1
 
0.1%
Cuban 1
 
0.1%
South/Central America (except Brazil) 1
 
0.1%
(Missing) 20
 
2.4%

Length

2025-07-28T11:38:42.425030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:42.495466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
non-spanish 728
39.8%
non-hispanic 728
39.8%
nos 114
 
6.2%
spanish 65
 
3.6%
hispanic 38
 
2.1%
latino 38
 
2.1%
unknown 27
 
1.5%
whether 27
 
1.5%
or 27
 
1.5%
not 27
 
1.5%
Other values (9) 11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n 3167
15.4%
i 2370
11.5%
a 1604
7.8%
o 1579
7.7%
N 1570
7.6%
p 1561
7.6%
s 1559
7.6%
- 1456
 
7.1%
1070
 
5.2%
S 908
 
4.4%
Other values (28) 3694
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 3167
15.4%
i 2370
11.5%
a 1604
7.8%
o 1579
7.7%
N 1570
7.6%
p 1561
7.6%
s 1559
7.6%
- 1456
 
7.1%
1070
 
5.2%
S 908
 
4.4%
Other values (28) 3694
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 3167
15.4%
i 2370
11.5%
a 1604
7.8%
o 1579
7.7%
N 1570
7.6%
p 1561
7.6%
s 1559
7.6%
- 1456
 
7.1%
1070
 
5.2%
S 908
 
4.4%
Other values (28) 3694
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 3167
15.4%
i 2370
11.5%
a 1604
7.8%
o 1579
7.7%
N 1570
7.6%
p 1561
7.6%
s 1559
7.6%
- 1456
 
7.1%
1070
 
5.2%
S 908
 
4.4%
Other values (28) 3694
18.0%

os_status
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.5%
Missing10
Missing (%)1.2%
Memory size6.5 KiB
LIVING
457 
DECEASED
213 
1:DECEASED
71 
0:LIVING
67 

Length

Max length10
Median length6
Mean length7.0445545
Min length6

Characters and Unicode

Total characters5.692
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1:DECEASED
2nd row1:DECEASED
3rd row1:DECEASED
4th row1:DECEASED
5th rowDECEASED

Common Values

ValueCountFrequency (%)
LIVING 457
55.9%
DECEASED 213
26.0%
1:DECEASED 71
 
8.7%
0:LIVING 67
 
8.2%
(Missing) 10
 
1.2%

Length

2025-07-28T11:38:42.579242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T11:38:42.640040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
living 457
56.6%
deceased 213
26.4%
1:deceased 71
 
8.8%
0:living 67
 
8.3%

Most occurring characters

ValueCountFrequency (%)
I 1048
18.4%
E 852
15.0%
D 568
10.0%
L 524
9.2%
V 524
9.2%
G 524
9.2%
N 524
9.2%
C 284
 
5.0%
A 284
 
5.0%
S 284
 
5.0%
Other values (3) 276
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1048
18.4%
E 852
15.0%
D 568
10.0%
L 524
9.2%
V 524
9.2%
G 524
9.2%
N 524
9.2%
C 284
 
5.0%
A 284
 
5.0%
S 284
 
5.0%
Other values (3) 276
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1048
18.4%
E 852
15.0%
D 568
10.0%
L 524
9.2%
V 524
9.2%
G 524
9.2%
N 524
9.2%
C 284
 
5.0%
A 284
 
5.0%
S 284
 
5.0%
Other values (3) 276
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1048
18.4%
E 852
15.0%
D 568
10.0%
L 524
9.2%
V 524
9.2%
G 524
9.2%
N 524
9.2%
C 284
 
5.0%
A 284
 
5.0%
S 284
 
5.0%
Other values (3) 276
 
4.8%

os_months
Real number (ℝ)

Missing 

Distinct478
Distinct (%)62.6%
Missing55
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean40.137544
Minimum0.066
Maximum99.879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2025-07-28T11:38:42.716812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.066
5-th percentile4.241
Q117.737
median34.718
Q360.2465
95-th percentile92.6296
Maximum99.879
Range99.813
Interquartile range (IQR)42.5095

Descriptive statistics

Standard deviation27.699129
Coefficient of variation (CV)0.69010523
Kurtosis-0.78500702
Mean40.137544
Median Absolute Deviation (MAD)21.14
Skewness0.53627044
Sum30624.946
Variance767.24175
MonotonicityNot monotonic
2025-07-28T11:38:42.815998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97.348 22
 
2.7%
87.09 18
 
2.2%
12.164 10
 
1.2%
11.079 9
 
1.1%
30.247 9
 
1.1%
63.123 8
 
1.0%
6.411 8
 
1.0%
34.751 8
 
1.0%
2.762 7
 
0.9%
21.699 7
 
0.9%
Other values (468) 657
80.3%
(Missing) 55
 
6.7%
ValueCountFrequency (%)
0.066 1
0.1%
0.132 1
0.1%
0.296 1
0.1%
0.427 1
0.1%
0.46 1
0.1%
0.921 2
0.2%
1.085 1
0.1%
1.348 1
0.1%
1.644 1
0.1%
1.742 1
0.1%
ValueCountFrequency (%)
99.879 1
 
0.1%
99.189 4
 
0.5%
97.348 22
2.7%
97.151 1
 
0.1%
95.901 1
 
0.1%
95.277 1
 
0.1%
94.356 1
 
0.1%
94.159 5
 
0.6%
92.909 1
 
0.1%
92.646 2
 
0.2%

Interactions

2025-07-28T11:38:30.010401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:13.066033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-28T11:38:14.856380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:15.795952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:16.918190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-28T11:38:14.116086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-28T11:38:17.073541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:18.039350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:19.025019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.110001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.926099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-28T11:38:23.882599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-28T11:38:27.001029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:28.141726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-28T11:38:30.211206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:13.275109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-28T11:38:15.080472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-28T11:38:15.416096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:16.514520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:17.526324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:18.469891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:19.589200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.479661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:21.350374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:22.462357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:23.352304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:24.355222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:25.431690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:26.436236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:27.446251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:28.548147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:29.605419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:30.628433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:13.666134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:14.549549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:15.466249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:16.566502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:17.585192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:18.518630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:19.638425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.526135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:21.437741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:22.513134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:23.403925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:24.403044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:25.473681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:26.486285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:27.493976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:28.589214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:29.652746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:30.678676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:13.716244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:14.589144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:15.512995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:16.615624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:17.638726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:18.570591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:19.686805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.569752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:21.486727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:22.569072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:23.458221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:24.462688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:25.516456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:26.536306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:27.548544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:28.640510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:29.698475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:30.736304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:13.766454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:14.636214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:15.567143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:16.673602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:17.692808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:18.627136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:19.747937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.617204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:21.545337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:22.631628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:23.518959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:24.536667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:25.558850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:26.598159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:27.609030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:28.706592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:29.765315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:30.796284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:13.820315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:14.686695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:15.627181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:16.737719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:17.746650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:18.689247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:19.800909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.662529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:21.597295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:22.680639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:23.575134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:24.589886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:25.607366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:26.657554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:27.670963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:28.785113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:29.828385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:30.855514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:13.875309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:14.758444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:15.685302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:16.801895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:17.796736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:18.736656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:19.856056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.707107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:21.649342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:22.730315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:23.629880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:24.649577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:25.663451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:26.716543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:27.735333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:28.849437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:29.886196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:31.044589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:13.926263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:14.809255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:15.744325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:16.863609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:17.849251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:18.803949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:19.906631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:20.752765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:21.702017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:22.775935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:23.683539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:24.707569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:25.717047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:26.778743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:27.796231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:28.928815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T11:38:29.952507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-28T11:38:43.114162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGENTBreakpoint_TypeClassConnection_TypeEVENT_TYPENOTENormal_Read_CountSTART_DATESTOP_DATESUBTYPESV_LengthSite1_ChromosomeSite1_PositionSite2_ChromosomeSite2_PositionTREATMENT_BEST_RESPONSETumor_Paired_End_Read_CountTumor_Read_CountTumor_Split_Read_CountTumor_Variant_Countage_at_diagnosisethnicityfirst_treatment_miotic_rate_50hpffirst_treatment_tumor_size_cmgene_panelinstitutemetastatic_sitemsi_scoremsi_typeos_adjuvanttherapyos_monthsos_statusped_indpre_therapy_groupprimary_siteracereligionrfs_monthsrfs_statusrisk_groupsample_coveragesample_typesexstage_at_diagnosistmb_nonsynonymoustumor_purity
AGENT1.0000.0000.0000.0001.0000.6020.0000.4950.4540.5991.0000.0000.0000.0000.0000.3750.0000.0000.0000.0000.1920.1580.0000.0000.0001.0000.1980.0000.0000.1360.1460.1061.0000.1870.0000.0000.0000.0000.2760.0000.0000.1890.0890.0000.0950.000
Breakpoint_Type0.0001.0000.0960.0001.0001.0000.0001.0001.0000.0000.0000.3130.2530.0000.0000.0000.4120.0810.9350.4640.8530.0000.0000.0000.0000.0000.0000.0000.0000.0000.4890.0001.0001.0000.0000.1750.3550.0000.0001.0000.0000.0000.0000.0000.5030.448
Class0.0000.0961.0000.6051.0001.0000.3211.0001.0000.0000.1670.4680.3280.4950.2870.0000.1790.4380.3030.1690.0000.0000.0000.5390.5310.0000.2820.3220.1810.4590.2610.1191.0000.7070.1510.1350.1320.0000.0001.0000.3170.2130.2410.0000.0000.191
Connection_Type0.0000.0000.6051.0001.0001.0000.2811.0001.0000.0000.1100.3940.3550.2890.4730.0990.2290.2630.0000.0000.0000.0000.1890.6240.2860.0000.4430.6430.1980.1830.2620.4621.0000.8660.4040.3580.3590.0000.0001.0000.3910.3350.4680.0000.3730.000
EVENT_TYPE1.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0000.0000.1031.0000.0000.0000.0000.0790.0000.0001.0000.0000.1340.0000.0000.6890.0591.0000.0000.0000.0000.0000.0000.340
NOTE0.6021.0001.0001.0001.0001.0001.0000.2210.1630.6131.0001.0001.0001.0001.0000.2851.0001.0001.0001.0000.3960.0000.1350.0000.1921.0000.3950.0000.0000.0000.2820.1001.0000.2470.0000.1730.0000.2380.0950.0000.0000.1490.1660.0000.0000.000
Normal_Read_Count0.0000.0000.3210.2811.0001.0001.0000.1280.2260.0000.9600.609-0.2080.595-0.0010.0000.3420.9790.480-0.156-0.6690.0000.439-0.3710.6870.0000.697-0.1670.0000.000-0.2980.0001.0000.0000.3990.0000.6350.1220.0001.000-0.3510.5140.2390.000-0.0190.231
START_DATE0.4951.0001.0001.0000.0000.2210.1281.0000.7870.1610.1371.0000.1001.000-0.1240.0830.1790.0671.0000.207-0.3500.000-0.1360.0170.0371.0000.7250.0610.0000.000-0.0730.0991.0001.0000.0720.3720.0000.0900.0171.0000.0810.2180.2060.0560.006-0.083
STOP_DATE0.4541.0001.0001.0001.0000.1630.2260.7871.0000.0860.2521.0000.0361.000-0.0610.0000.2930.1641.0000.139-0.3750.097-0.231-0.0410.0001.0000.7120.0310.0000.000-0.0390.0761.0000.0000.0160.4060.0000.0670.0001.0000.0380.1940.1610.0000.032-0.115
SUBTYPE0.5990.0000.0000.0001.0000.6130.0000.1610.0861.0001.0000.0000.0000.0000.0000.4150.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0070.0001.0000.0000.0000.0000.0000.0000.2380.0000.0000.1840.0000.0000.0000.000
SV_Length1.0000.0000.1670.1101.0001.0000.9600.1370.2521.0001.0000.439-0.1440.4090.0561.0000.3630.9420.000-0.105-0.7020.1610.509-0.3800.0000.5140.584-0.1250.0000.000-0.2580.0171.0000.0000.2980.0000.5370.0190.4831.000-0.2730.4660.1750.398-0.0290.324
Site1_Chromosome0.0000.3130.4680.3941.0001.0000.6091.0001.0000.0000.4391.0000.6250.7470.6000.0000.1630.4980.4410.3760.0000.3420.3900.0000.5710.0000.4750.3110.4240.5220.3950.3531.0000.0000.4950.5380.4870.3330.0001.0000.5310.5540.3330.0000.4650.494
Site1_Position0.0000.2530.3280.3551.0001.000-0.2080.1000.0360.000-0.1440.6251.0000.5300.7230.000-0.028-0.1930.0000.0910.0390.319-0.232-0.2780.0000.2430.0000.1430.1160.5320.2320.3111.0000.5000.4320.1010.0000.0110.0001.000-0.0700.2530.3430.000-0.058-0.016
Site2_Chromosome0.0000.0000.4950.2891.0001.0000.5951.0001.0000.0000.4090.7470.5301.0000.4670.0000.3360.4940.4120.5020.0000.2220.4980.1360.5090.4200.5210.3910.5780.2750.3840.4281.0000.5000.4310.1730.3720.3330.2181.0000.3810.4590.1790.2180.4190.170
Site2_Position0.0000.0000.2870.4731.0001.000-0.001-0.124-0.0610.0000.0560.6000.7230.4671.0000.000-0.0550.0150.000-0.241-0.0170.000-0.179-0.1460.0000.0000.2620.1660.5500.389-0.1710.5291.0000.5000.4360.5120.207-0.1630.1971.0000.0700.6670.0000.000-0.0250.127
TREATMENT_BEST_RESPONSE0.3750.0000.0000.0991.0000.2850.0000.0830.0000.4151.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.1220.1170.3110.0640.1251.0000.0970.0000.1090.1480.2840.1241.0000.3650.1680.1530.1480.1650.7020.2650.0410.2320.0000.3200.1020.000
Tumor_Paired_End_Read_Count0.0000.4120.1790.2291.0001.0000.3420.1790.2930.0000.3630.163-0.0280.336-0.0550.0001.0000.2600.0000.995-0.3830.0000.280-0.4920.2530.0000.1600.2230.0000.0000.1710.0001.0000.8660.1920.0000.1340.0640.4791.0000.2170.2450.4660.7450.2020.192
Tumor_Read_Count0.0000.0810.4380.2631.0001.0000.9790.0670.1640.0000.9420.498-0.1930.4940.0150.0000.2601.0000.363-0.217-0.7350.0000.414-0.3770.3400.0000.738-0.1830.0000.174-0.3100.0001.0000.0000.2840.0000.6070.1250.0001.000-0.3100.5300.2330.000-0.0460.189
Tumor_Split_Read_Count0.0000.9350.3030.0001.0001.0000.4801.0001.0000.0000.0000.4410.0000.4120.0000.0000.0000.3631.0000.0000.0000.1080.0000.0000.7380.0000.3160.0000.0000.0000.3010.0331.0000.0000.0000.0000.3970.0000.0001.0000.0000.0000.0000.2080.3790.528
Tumor_Variant_Count0.0000.4640.1690.0001.0001.000-0.1560.2070.1390.000-0.1050.3760.0910.502-0.2410.0000.995-0.2170.0001.000-0.2650.1800.116-0.3990.1600.0000.2160.1600.0000.0000.4260.3221.0000.0000.2440.0710.378-0.0330.0001.0000.1180.1840.2830.0000.041-0.120
age_at_diagnosis0.1920.8530.0000.0000.0000.396-0.669-0.350-0.3750.000-0.7020.0000.0390.000-0.0170.122-0.383-0.7350.000-0.2651.0000.000-0.033-0.0280.2070.0770.179-0.0070.0220.311-0.0170.2140.7630.4650.1130.0520.131-0.0130.2170.000-0.1610.1870.0870.2010.0710.090
ethnicity0.1580.0000.0000.0000.0000.0000.0000.0000.0970.0000.1610.3420.3190.2220.0000.1170.0000.0000.1080.1800.0001.0000.0000.0000.0240.0000.1780.0000.0320.0750.0590.0000.0000.0900.4350.1400.3960.0000.0000.0810.0000.0000.1370.0000.0000.073
first_treatment_miotic_rate_50hpf0.0000.0000.0000.1891.0000.1350.439-0.136-0.2310.0000.5090.390-0.2320.498-0.1790.3110.2800.4140.0000.116-0.0330.0001.0000.2430.1900.0000.0000.3500.0690.237-0.0390.1480.0000.0000.0000.0250.302-0.1400.3170.0590.0220.0000.1340.3540.1380.117
first_treatment_tumor_size_cm0.0000.0000.5390.6240.0000.000-0.3710.017-0.0410.000-0.3800.000-0.2780.136-0.1460.064-0.492-0.3770.000-0.399-0.0280.0000.2431.0000.1400.0000.1530.3250.1170.346-0.0480.1680.0000.3330.0930.0390.269-0.1460.3830.1000.0510.0750.1200.2350.1070.003
gene_panel0.0000.0000.5310.2860.1030.1920.6870.0370.0000.0000.0000.5710.0000.5090.0000.1250.2530.3400.7380.1600.2070.0240.1900.1401.0000.0820.3300.1400.2160.3370.3540.1950.0650.3940.3000.0800.2250.2050.3720.1350.3610.1880.0000.1390.2010.146
institute1.0000.0000.0000.0001.0001.0000.0001.0001.0001.0000.5140.0000.2430.4200.0001.0000.0000.0000.0000.0000.0770.0000.0000.0000.0821.0000.4010.0000.0000.0670.0810.0490.2140.0650.2950.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
metastatic_site0.1980.0000.2820.4430.0000.3950.6970.7250.7120.0000.5840.4750.0000.5210.2620.0970.1600.7380.3160.2160.1790.1780.0000.1530.3300.4011.0000.2970.3550.2530.2400.2540.0000.3960.3340.1330.1880.1260.5420.2400.2460.5150.2320.4500.3640.199
msi_score0.0000.0000.3220.6430.0000.000-0.1670.0610.0310.000-0.1250.3110.1430.3910.1660.0000.223-0.1830.0000.160-0.0070.0000.3500.3250.1400.0000.2971.0000.5900.112-0.1240.2010.0000.1150.0900.0290.211-0.1180.2980.0990.1220.1410.0000.1490.2170.259
msi_type0.0000.0000.1810.1980.0000.0000.0000.0000.0000.0000.0000.4240.1160.5780.5500.1090.0000.0000.0000.0000.0220.0320.0690.1170.2160.0000.3550.5901.0000.0000.2330.1220.0000.0610.1490.0530.1140.0730.0660.0000.0830.0570.0260.0690.0600.103
os_adjuvanttherapy0.1360.0000.4590.1830.0790.0000.0000.0000.0000.0000.0000.5220.5320.2750.3890.1480.0000.1740.0000.0000.3110.0750.2370.3460.3370.0670.2530.1120.0001.0000.0000.2590.0000.7940.2720.0000.1220.1030.3580.1550.1630.2970.0720.1360.2200.085
os_months0.1460.4890.2610.2620.0000.282-0.298-0.073-0.0390.007-0.2580.3950.2320.384-0.1710.2840.171-0.3100.3010.426-0.0170.059-0.039-0.0480.3540.0810.240-0.1240.2330.0001.0000.2940.0000.1720.1500.0870.1610.1810.2350.0750.1110.1480.1830.162-0.1280.043
os_status0.1060.0000.1190.4620.0000.1000.0000.0990.0760.0000.0170.3530.3110.4280.5290.1240.0000.0000.0330.3220.2140.0000.1480.1680.1950.0490.2540.2010.1220.2590.2941.0000.0280.0000.4300.0240.1970.1740.5280.0920.2480.2070.1150.3760.2830.100
ped_ind1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7630.0000.0000.0000.0650.2140.0000.0000.0000.0000.0000.0281.0001.0000.0000.1720.0000.4330.0000.0000.1210.0470.0000.0000.0000.109
pre_therapy_group0.1871.0000.7070.8660.0000.2470.0001.0000.0000.0000.0000.0000.5000.5000.5000.3650.8660.0000.0000.0000.4650.0900.0000.3330.3940.0650.3960.1150.0610.7940.1720.0001.0001.0000.2350.0670.2530.2080.1951.0000.1000.2140.0000.3740.0000.137
primary_site0.0000.0000.1510.4040.1340.0000.3990.0720.0160.0000.2980.4950.4320.4310.4360.1680.1920.2840.0000.2440.1130.4350.0000.0930.3000.2950.3340.0900.1490.2720.1500.4300.0000.2351.0000.1410.1320.0840.3610.0000.1710.1520.1080.2550.2640.161
race0.0000.1750.1350.3580.0000.1730.0000.3720.4060.0000.0000.5380.1010.1730.5120.1530.0000.0000.0000.0710.0520.1400.0250.0390.0800.0690.1330.0290.0530.0000.0870.0240.1720.0670.1411.0000.3100.1180.0800.0600.0300.0200.1490.1030.4030.083
religion0.0000.3550.1320.3590.0000.0000.6350.0000.0000.0000.5370.4870.0000.3720.2070.1480.1340.6070.3970.3780.1310.3960.3020.2690.2250.0000.1880.2110.1140.1220.1610.1970.0000.2530.1320.3101.0000.0590.2510.0730.1600.1920.1270.2410.2230.079
rfs_months0.0000.0000.0000.0000.6890.2380.1220.0900.0670.0000.0190.3330.0110.333-0.1630.1650.0640.1250.000-0.033-0.0130.000-0.140-0.1460.2050.0000.126-0.1180.0730.1030.1810.1740.4330.2080.0840.1180.0591.0000.1670.062-0.0180.1020.1010.454-0.023-0.063
rfs_status0.2760.0000.0000.0000.0590.0950.0000.0170.0000.2380.4830.0000.0000.2180.1970.7020.4790.0000.0000.0000.2170.0000.3170.3830.3720.0000.5420.2980.0660.3580.2350.5280.0000.1950.3610.0800.2510.1671.0000.4590.2270.5730.0850.6160.2560.077
risk_group0.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.2651.0001.0001.0001.0000.0000.0810.0590.1000.1350.0000.2400.0990.0000.1550.0750.0920.0001.0000.0000.0600.0730.0620.4591.0000.1550.2500.1590.9960.0860.126
sample_coverage0.0000.0000.3170.3910.0000.000-0.3510.0810.0380.000-0.2730.531-0.0700.3810.0700.0410.217-0.3100.0000.118-0.1610.0000.0220.0510.3610.0000.2460.1220.0830.1630.1110.2480.1210.1000.1710.0300.160-0.0180.2270.1551.0000.2270.1530.2290.1480.079
sample_type0.1890.0000.2130.3350.0000.1490.5140.2180.1940.1840.4660.5540.2530.4590.6670.2320.2450.5300.0000.1840.1870.0000.0000.0750.1880.0000.5150.1410.0570.2970.1480.2070.0470.2140.1520.0200.1920.1020.5730.2500.2271.0000.0000.3500.2080.005
sex0.0890.0000.2410.4680.0000.1660.2390.2060.1610.0000.1750.3330.3430.1790.0000.0000.4660.2330.0000.2830.0870.1370.1340.1200.0000.0000.2320.0000.0260.0720.1830.1150.0000.0000.1080.1490.1270.1010.0850.1590.1530.0001.0000.0990.1190.024
stage_at_diagnosis0.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.3980.0000.0000.2180.0000.3200.7450.0000.2080.0000.2010.0000.3540.2350.1390.0000.4500.1490.0690.1360.1620.3760.0000.3740.2550.1030.2410.4540.6160.9960.2290.3500.0991.0000.1770.080
tmb_nonsynonymous0.0950.5030.0000.3730.0000.000-0.0190.0060.0320.000-0.0290.465-0.0580.419-0.0250.1020.202-0.0460.3790.0410.0710.0000.1380.1070.2010.0000.3640.2170.0600.220-0.1280.2830.0000.0000.2640.4030.223-0.0230.2560.0860.1480.2080.1190.1771.0000.021
tumor_purity0.0000.4480.1910.0000.3400.0000.231-0.083-0.1150.0000.3240.494-0.0160.1700.1270.0000.1920.1890.528-0.1200.0900.0730.1170.0030.1460.0000.1990.2590.1030.0850.0430.1000.1090.1370.1610.0830.079-0.0630.0770.1260.0790.0050.0240.0800.0211.000

Missing values

2025-07-28T11:38:31.206649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-28T11:38:31.491972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-28T11:38:32.071965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sample_idpatient_idinstitutereligionped_indrfs_statusrfs_monthsage_at_diagnosisstage_at_diagnosisfirst_treatment_tumor_size_cmfirst_treatment_miotic_rate_50hpfpre_therapy_groupos_adjuvanttherapyrisk_groupSTART_DATESTOP_DATEEVENT_TYPESUBTYPEAGENTTREATMENT_BEST_RESPONSENOTETREATMENT_DETAILSSV_StatusSite1_Hugo_SymbolSite2_Hugo_SymbolSite1_ChromosomeSite2_ChromosomeSite1_PositionSite2_PositionSite1_DescriptionSite2_DescriptionNCBI_BuildClassTumor_Split_Read_CountTumor_Paired_End_Read_CountEvent_InfoBreakpoint_TypeConnection_TypeAnnotationDNA_SupportRNA_SupportSV_LengthNormal_Read_CountTumor_Read_CountNormal_Variant_CountTumor_Variant_CountCommentscancer_typesample_typesample_classmetastatic_siteprimary_sitecancer_type_detailedgene_panelsample_coveragetumor_purityoncotree_codemsi_scoremsi_typesomatic_statusage_at_seq_reported_yearstmb_nonsynonymousgenes_mutadosracesexethnicityos_statusos_months
0P-0000134-T01-IM3P-0000134NaNNaNNaN1:Recurrence12.068.0Metastatic13.650.0NeoadjuvantYesNaN1.0396.0TREATMENTLINE 1 PRE IMPACTIMATINIBYES(pr/sd X >/=6 MONTHS)NaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT341761.090.0GIST5.07IndeterminateMatched69.00.133333['KIT', 'RB1', 'TP53']WHITEFemaleNon-Spanish; Non-Hispanic1:DECEASED11.079
1P-0000134-T01-IM3P-0000134NaNNaNNaN1:Recurrence12.068.0Metastatic13.650.0NeoadjuvantYesNaN608.0670.0TREATMENTLINE 2 POST IMPACTREGORAFENIBNONaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT341761.090.0GIST5.07IndeterminateMatched69.00.133333['KIT', 'RB1', 'TP53']WHITEFemaleNon-Spanish; Non-Hispanic1:DECEASED11.079
2P-0000134-T01-IM3P-0000134NaNNaNNaN1:Recurrence12.068.0Metastatic13.650.0NeoadjuvantYesNaN731.0792.0TREATMENTLINE 3 POST IMPACTCLINICAL TRIALNONaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT341761.090.0GIST5.07IndeterminateMatched69.00.133333['KIT', 'RB1', 'TP53']WHITEFemaleNon-Spanish; Non-Hispanic1:DECEASED11.079
3P-0000134-T01-IM3P-0000134NaNNaNNaN1:Recurrence12.068.0Metastatic13.650.0NeoadjuvantYesNaN396.0578.0TREATMENTLINE 1 POST IMPACTSUNITINIB AND EVEROLIMUSNONaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT341761.090.0GIST5.07IndeterminateMatched69.00.133333['KIT', 'RB1', 'TP53']WHITEFemaleNon-Spanish; Non-Hispanic1:DECEASED11.079
4P-0000134-T02-IM3P-0000134MSKCCJEWISHNoNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0396.0TREATMENTLINE 1 PRE IMPACTIMATINIBYES(pr/sd X >/=6 MONTHS)NaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorMetastasisTumorLiverStomachGastrointestinal Stromal TumorIMPACT341661.0NaNGIST3.04IndeterminateMatched702.218311['RB1', 'TP53']WHITEFemaleNon-Spanish; Non-HispanicDECEASED11.079
5P-0000134-T02-IM3P-0000134MSKCCJEWISHNoNaNNaNNaNNaNNaNNaNNaNNaNNaN608.0670.0TREATMENTLINE 2 POST IMPACTREGORAFENIBNONaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorMetastasisTumorLiverStomachGastrointestinal Stromal TumorIMPACT341661.0NaNGIST3.04IndeterminateMatched702.218311['RB1', 'TP53']WHITEFemaleNon-Spanish; Non-HispanicDECEASED11.079
6P-0000134-T02-IM3P-0000134MSKCCJEWISHNoNaNNaNNaNNaNNaNNaNNaNNaNNaN731.0792.0TREATMENTLINE 3 POST IMPACTCLINICAL TRIALNONaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorMetastasisTumorLiverStomachGastrointestinal Stromal TumorIMPACT341661.0NaNGIST3.04IndeterminateMatched702.218311['RB1', 'TP53']WHITEFemaleNon-Spanish; Non-HispanicDECEASED11.079
7P-0000134-T02-IM3P-0000134MSKCCJEWISHNoNaNNaNNaNNaNNaNNaNNaNNaNNaN396.0578.0TREATMENTLINE 1 POST IMPACTSUNITINIB AND EVEROLIMUSNONaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorMetastasisTumorLiverStomachGastrointestinal Stromal TumorIMPACT341661.0NaNGIST3.04IndeterminateMatched702.218311['RB1', 'TP53']WHITEFemaleNon-Spanish; Non-HispanicDECEASED11.079
8P-0000306-T01-IM3P-0000306MSKCCNONENo1:Recurrence89.048.0Localized13.05.0NaNNoModerate-967.0409.0TREATMENTLINE 1 POST IMPACTIMATINIBYESONGOINGMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableStomachGastrointestinal Stromal TumorIMPACT341212.090.0GIST0.76StableMatched571.109155['KIT']WHITEMaleNon-Spanish; Non-HispanicLIVING92.351
9P-0000309-T03-IM5P-0000309MSKCCCATHOLIC/ROMANNoNaNNaNNaNNaNNaNNaNNaNNaNNaN39462.039615.0TREATMENTLINE 1 PRE IMPACTIMATINIBNONaNMedical TherapyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorMetastasisTumorLungSmall BowelGastrointestinal Stromal TumorIMPACT410893.080.0GIST1.11StableMatched600.978720['KMT2B']ASIAN-FAR EAST/INDIAN SUBCONTFemaleNon-Spanish; Non-HispanicLIVING34.718
sample_idpatient_idinstitutereligionped_indrfs_statusrfs_monthsage_at_diagnosisstage_at_diagnosisfirst_treatment_tumor_size_cmfirst_treatment_miotic_rate_50hpfpre_therapy_groupos_adjuvanttherapyrisk_groupSTART_DATESTOP_DATEEVENT_TYPESUBTYPEAGENTTREATMENT_BEST_RESPONSENOTETREATMENT_DETAILSSV_StatusSite1_Hugo_SymbolSite2_Hugo_SymbolSite1_ChromosomeSite2_ChromosomeSite1_PositionSite2_PositionSite1_DescriptionSite2_DescriptionNCBI_BuildClassTumor_Split_Read_CountTumor_Paired_End_Read_CountEvent_InfoBreakpoint_TypeConnection_TypeAnnotationDNA_SupportRNA_SupportSV_LengthNormal_Read_CountTumor_Read_CountNormal_Variant_CountTumor_Variant_CountCommentscancer_typesample_typesample_classmetastatic_siteprimary_sitecancer_type_detailedgene_panelsample_coveragetumor_purityoncotree_codemsi_scoremsi_typesomatic_statusage_at_seq_reported_yearstmb_nonsynonymousgenes_mutadosracesexethnicityos_statusos_months
808P-0068742-T01-IM7P-0068742NaNNaNNaN0:No recurrence14.077.0Localized13.51.0NeoadjuvantYesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT505415.020.0GIST0.02StableMatched78.00.033333['KIT']ASIAN-FAR EAST/INDIAN SUBCONTFemaleNon-Spanish; Non-Hispanic0:LIVING13.775
809P-0069520-T01-IM7P-0069520NaNNaNNaN0:No recurrence5.071.0Localized6.02.0NaNNoModerateNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT505402.080.0GIST0.24StableMatchedNaN0.033333['KIT']BLACK OR AFRICAN AMERICANFemaleNon-Spanish; Non-Hispanic0:LIVINGNaN
810P-0069950-T01-IM7P-0069950NaNNaNNaN0:No recurrence0.070.0Localized5.62.0NaNNoModerateNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT505479.070.0GIST0.06StableMatchedNaN0.033333['PDGFRA']WHITEMaleNon-Spanish; Non-Hispanic0:LIVINGNaN
811P-0070054-T01-IM7P-0070054NaNNaNNaN1:Recurrence0.078.0Metastatic9.2NaNPalliativeNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorLiverGastricGastrointestinal Stromal TumorIMPACT505283.060.0GIST0.20StableMatchedNaN0.033333['ROS1']WHITEMaleSpanish NOS; Hispanic NOS, Latino NOS1:DECEASEDNaN
812P-0070183-T01-IM7P-0070183NaNNaNNaN0:No recurrence2.060.0Localized6.21.0NaNNoHighNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT505557.080.0GIST0.24StableMatchedNaN0.033333['PDGFRA']BLACK OR AFRICAN AMERICANFemaleNon-Spanish; Non-Hispanic0:LIVINGNaN
813P-0071460-T02-IM7P-0071460NaNNaNNaN1:Recurrence0.074.0Metastatic13.1NaNPalliativeNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorSoft TissueGastricGastrointestinal Stromal TumorIMPACT505418.090.0GIST3.58IndeterminateMatched74.00.100000['KIT']WHITEMaleNon-Spanish; Non-Hispanic1:DECEASED2.499
814P-0071507-T01-IM7P-0071507NaNNaNNaN0:No recurrence1.059.0Localized5.33.0NaNNoModerateNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT505657.090.0GIST0.11StableMatched59.00.033333['PDGFRA']PT REFUSED TO ANSWERFemaleUnknown whether Spanish or not0:LIVING13.151
815P-0072264-T01-IM7P-0072264NaNNaNNaN0:No recurrence8.075.0Localized6.54.0NaNNoLowNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT505505.090.0GIST0.18StableMatched76.00.066667['KIT', 'RPTOR']OTHERFemaleSpanish NOS; Hispanic NOS, Latino NOS0:LIVING16.636
816P-0072497-T01-IM7P-0072497NaNNaNNaN1:Recurrence0.060.0Localized18.011.0PalliativeNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableGastricGastrointestinal Stromal TumorIMPACT505511.080.0GIST3.34IndeterminateMatched77.00.100000['APLNR', 'KIT']BLACK OR AFRICAN AMERICANFemaleSpanish NOS; Hispanic NOS, Latino NOS0:LIVING17.721
817P-0074085-T01-IM7P-0074085NaNNaNNaN1:Recurrence0.072.0Metastatic9.216.0PalliativeNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGastrointestinal Stromal TumorPrimaryTumorNot ApplicableSmall BowelGastrointestinal Stromal TumorIMPACT505350.080.0GIST1.88StableMatched72.00.166667['PDGFRA', 'PIK3R1', 'SRSF2', 'TET2', 'TP53']WHITEFemaleNon-Spanish; Non-Hispanic1:DECEASED0.460